@Article{info:doi/10.2196/60084,
author="Gebreyohannes, Alemayehu Eyob
and Thornton, Christopher
and Thiessen, Myra
and de Vries, T. Sieta
and Q Andrade, Andre
and Kalisch Ellett, Lisa
and Frank, Oliver
and Cheah, Yeong Phaik
and Choo, Raymond Kim-Kwang
and Laba, Lea Tracey
and Roughead, E. Elizabeth
and Hwang, Indae
and Moses, Geraldine
and Lim, Renly",
title="Co-Designing a Consumer-Focused Digital Reporting Health Platform to Improve Adverse Medicine Event Reporting: Protocol for a Multimethod Research Project (the ReMedi Project)",
journal="JMIR Res Protoc",
year="2025",
month="Jan",
day="15",
volume="14",
pages="e60084",
keywords="adverse drug events",
keywords="drug-related side effects and adverse reactions",
keywords="adverse drug reaction reporting systems",
keywords="pharmacovigilance",
keywords="digital health",
keywords="medication safety",
keywords="co-design",
keywords="qualitative research, user-centered design",
abstract="Background: Adverse medicine events (AMEs) are unintended effects that occur following administration of medicines. Up to 70\% of AMEs are not reported to, and hence remain undetected by, health care professionals and only 6\% of AMEs are reported to regulators. Increased reporting by consumers, health care professionals, and pharmaceutical companies to medicine regulatory authorities is needed to increase the safety of medicines. Objective: We describe a project that aims to co-design a digital reporting platform to improve detection and management of AMEs by consumers and health care professionals and improve reporting to regulators. Methods: The project will be conducted in 3 phases and uses a co-design methodology that prioritizes equity in designing with stakeholders. Our project is guided by the Consolidated Framework for Implementation Research. In phase 1, we will engage with 3 stakeholder groups---consumers, health care professionals, and regulators---to define digital platform development standards. We will conduct a series of individual interviews, focus group discussions, and co-design workshops with the stakeholder groups. In phase 2, we will work with a software developer and user interaction design experts to prototype, test, and develop the digital reporting platform based on findings from phase 1. In phase 3, we will implement and trial the digital reporting platform in South Australia through general practices and pharmacies. Consumers who have recently started using medicines new to them will be recruited to use the digital reporting platform to report any apparent, suspected, or possible AMEs since starting the new medicine. Process and outcome evaluations will be conducted to assess the implementation process and to determine whether the new platform has increased AME detection and reporting. Results: This project, initiated in 2023, will run until 2026. Phase 1 will result in persona profiles and user journey maps that define the standards for the user-friendly platform and interactive data visualization tool or dashboard that will be developed and further improved in phase 2. Finally, phase 3 will provide insights of the implemented platform regarding its impact on AME detection, management, and reporting. Findings will be published progressively as we complete the different phases of the project. Conclusions: This project adopts a co-design methodology to develop a new digital reporting platform for AME detection and reporting, considering the perspectives and lived experience of stakeholders and addressing their requirements throughout the entire process. The overarching goal of the project is to leverage the potential of both consumers and technology to address the existing challenges of underdetection and underreporting of AMEs to health care professionals and regulators. The project potentially will improve individual patient safety and generate new data for regulatory purposes related to medicine safety and effectiveness. International Registered Report Identifier (IRRID): DERR1-10.2196/60084 ",
doi="10.2196/60084",
url="https://www.researchprotocols.org/2025/1/e60084"
}


@Article{info:doi/10.2196/65725,
author="Weimar, Noel Sascha
and Martjan, Sophie Rahel
and Terzidis, Orestis",
title="Business Venturing in Regulated Markets---Taxonomy and Archetypes of Digital Health Business Models in the European Union: Mixed Methods Descriptive and Exploratory Study",
journal="J Med Internet Res",
year="2025",
month="Jan",
day="9",
volume="27",
pages="e65725",
keywords="digital health",
keywords="telemedicine",
keywords="mobile health",
keywords="business model",
keywords="European Union",
keywords="classification",
keywords="archetypes",
keywords="medical device regulations",
keywords="mobile phone",
keywords="artificial intelligence",
keywords="AI",
abstract="Background: Digital health technology (DHT) has the potential to revolutionize the health care industry by reducing costs and improving the quality of care in a sector that faces significant challenges. However, the health care industry is complex, involving numerous stakeholders, and subject to extensive regulation. Within the European Union, medical device regulations impose stringent requirements on various ventures. Concurrently, new reimbursement pathways are also being developed for DHTs. In this dynamic context, establishing a sustainable and innovative business model around DHTs is fundamental for their successful commercialization. However, there is a notable lack of structured understanding regarding the overarching business models within the digital health sector. Objective: This study aims to address this gap and identify key elements and configurations of business models for DHTs in the European Union, thereby establishing a structured understanding of the archetypal business models in use. Methods: The study was conducted in 2 phases. First, a business model taxonomy for DHTs was developed based on a systematic literature review, the analysis of 169 European real-world business models, and qualitative evaluation through 13 expert interviews. Subsequently, a 2-step clustering analysis was conducted on the 169 DHT business models to identify distinct business model archetypes. Results: The developed taxonomy of DHT business models revealed 11 central dimensions organized into 4 meta-dimensions. Each dimension comprises 2 to 9 characteristics capturing relevant aspects of DHT business models. In addition, 6 archetypes of DHT business models were identified: administration and communication supporter (A1), insurer-to-consumer digital therapeutics and care (A2), diagnostic and treatment enabler (A3), professional monitoring platforms (A4), clinical research and solution accelerators (A5), and direct-to-consumer wellness and lifestyle (A6). Conclusions: The findings highlight the critical elements constituting business models in the DHT domain, emphasizing the substantial impact of medical device regulations and revenue models, which often involve reimbursement from stakeholders such as health insurers. Three drivers contributing to DHT business model innovation were identified: direct targeting of patients and private individuals, use of artificial intelligence as an enabler, and development of DHT-specific reimbursement pathways. The study also uncovered surprising business model patterns, including shifts between regulated medical devices and unregulated research applications, as well as wellness and lifestyle solutions. This research enriches the understanding of business models in digital health, offering valuable insights for researchers and digital health entrepreneurs. ",
doi="10.2196/65725",
url="https://www.jmir.org/2025/1/e65725"
}


@Article{info:doi/10.2196/57786,
author="Neunaber, Timo
and Mortsiefer, Achim
and Meister, Sven",
title="Dimensions and Subcategories of Digital Maturity in General Practice: Qualitative Study",
journal="J Med Internet Res",
year="2024",
month="Dec",
day="19",
volume="26",
pages="e57786",
keywords="digital health",
keywords="eHealth",
keywords="digital maturity",
keywords="maturity assessment",
keywords="primary care",
keywords="general practice",
keywords="general practitioner",
keywords="qualitative research",
keywords="expert interviews",
keywords="interview",
keywords="explorative",
keywords="dimension",
keywords="subcategory",
keywords="expert",
keywords="care provider",
keywords="content analysis",
abstract="Background: The status of the digitalization of companies and institutions is usually measured using maturity models. However, the concept of maturity in general practice is currently unclear, and herewith we examine the question of how maturity can be measured. There is a lack of empirical work on the dimensions and subcategories of digital maturity that provide information on the assessment framework. Objective: The aim of the study was to answer the question of how many and which dimensions and subcategories describe digital maturity in general practice. Methods: An explorative, qualitative research design based on semistructured expert interviews was used to investigate the dimensions of digital maturity. Twenty experts from various areas of the health care sector (care providers, interest groups, health care industry, and patient organizations) were interviewed. The interviews were analyzed based on a content-structuring analysis according to Kuckartz and R{\"a}diker using MAXQDA software (VERBI GmbH). Results: In total, 6 dimensions with a total of 26 subcategories were identified. Of these, 4 dimensions with a total of 16 subcategories (1) digitally supported processes, (2) practice staff, (3) organizational structures and rules, and (4) technical infrastructure and were deductively linked to digital maturity. In addition to the use of digital solutions, digital maturity included, for example, individual, organizational, and technical capabilities and resources of the medical practice. The 2 further dimensions, (5) benefits and outcomes and (6) external framework conditions of the medical practice, were identified inductively with a total of 10 subcategories. Digital maturity was associated with the beneficial use of digitalization, for example, with efficiency benefits for the practice, and external framework conditions were associated with influencing factors such as the local patient situation in the medical practice. Conclusions: The results indicate that digital maturity is a multidimensional construct that is associated with many dimensions and variables. It is a holistic approach with human, organizational, and technical factors and concerns the way digitalization is used to shape patient care and processes. Furthermore, it is related to the maturity of the organizational environment as well as the benefits of a digitalized medical practice; however, this still needs to be confirmed. To measure the level of digital maturity in outpatient care as accurately as possible, maturity models should therefore be multilayered and take external influencing factors into account. Future research should statistically validate the identified dimensions. At the same time, correlations and dependencies between the measurement dimensions and their subcategories should be analyzed. ",
doi="10.2196/57786",
url="https://www.jmir.org/2024/1/e57786",
url="http://www.ncbi.nlm.nih.gov/pubmed/39699948"
}


@Article{info:doi/10.2196/60473,
author="Pfitzer, Estelle
and Bitomsky, Laura
and Ni{\ss}en, Marcia
and Kausch, Christoph
and Kowatsch, Tobias",
title="Success Factors of Growth-Stage Digital Health Companies: Systematic Literature Review",
journal="J Med Internet Res",
year="2024",
month="Dec",
day="11",
volume="26",
pages="e60473",
keywords="digital health",
keywords="health information technology",
keywords="success factors",
keywords="systematic literature review",
keywords="growth-stage companies",
keywords="clinical softwares",
keywords="EHR",
keywords="electronic health records",
keywords="health companies",
keywords="funding",
keywords="digital therapeutics",
keywords="substance use disorder",
keywords="stakeholders",
abstract="Background: Over the past decade, digital health technologies (DHTs) have grown rapidly, driven by innovations such as electronic health records and accelerated by the COVID-19 pandemic. Increased funding and regulatory support have further pushed the sector's expansion. Despite early success, many DHT companies struggle to scale, with notable examples like Pear Therapeutics and Proteus Digital Health, which both declared bankruptcy after initial breakthroughs. These cases highlight the challenges of sustaining growth in a highly regulated health care environment. While there is research on success factors across industries, a gap remains in understanding the specific challenges faced by growth-stage DHT companies. Objective: This study aims to identify and discuss key factors that make growth-stage DHT companies successful. Specifically, we address three questions: (1) What are the success factors of growth-stage digital companies in general and (2) digital health companies in particular? (3) How do these success factors vary across DHTs? Methods: Following established PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a systematic literature review was conducted to answer the questions. A comprehensive literature search was conducted using management and medical literature databases: EBSCO, ProQuest, PubMed, Scopus, and Web of Science. The review spanned scientific articles published from 2000 to 2023, using a rigorous screening process and quality assessment using the Critical Appraisal Skills Programme (CASP) checklist. Results: From the 2972 studies initially screened, 36 were selected, revealing 52 success factors. We categorized them into internal factor categories (Product and Services, Operations, Business Models, and Team Composition) and external factor categories (Customers, Health Care System, Government and Regulators, Investors and Shareholders, Suppliers and Partners, and Competitors). Of the 52 factors, 19 were specific to DHT companies. The most frequently cited internal success factors included financial viability (n=18) and market demand and relevance of the product and service (n=13). External success factors emphasized the regulatory environment and policy framework (n=15). Key differences were observed between DHTs and broader digital companies in areas such as data security (P=.03), system interoperability (P=.01), and regulatory alignment (P=.02), with DHTs showing a higher frequency of these factors. In addition, success factors varied across different DHT categories. Health System Operational Software companies emphasized affordability and system integration, while Digital Therapeutics prioritized factors related to government regulations and regulatory approval. Conclusions: Essential characteristics contributing to the success of growth-stage digital health companies have been identified. This work, therefore, fills a knowledge gap and provides relevant stakeholders, including investors and entrepreneurs, with a valuable resource that can support informed decision-making in investment decisions and, in turn, enhance the success of fast-growing digital health companies. In addition, it provides the research community with a direction for future studies, enhancing the understanding, implementation, and growth of DHTs. International Registered Report Identifier (IRRID): RR2-10.1101/2024.05.06.24306674 ",
doi="10.2196/60473",
url="https://www.jmir.org/2024/1/e60473"
}


@Article{info:doi/10.2196/56327,
author="Ganeshan, Smitha
and Goldstein, Joshua
and Sohn, Young-Jin
and Pollack, Amie
and Phillips, S. Russell
and Rotenstein, Lisa",
title="Impact of COVID-19 on Characteristics and Funding of U.S. Healthcare Startups: Retrospective Review",
journal="JMIR Form Res",
year="2024",
month="Aug",
day="27",
volume="8",
pages="e56327",
keywords="artificial intelligence",
keywords="venture capital",
keywords="COVID-19",
keywords="health care startup",
keywords="health care",
keywords="AI",
keywords="retrospective study",
keywords="telehealth",
keywords="telemedicine",
keywords="pandemic",
keywords="patients",
keywords="digital health",
keywords="coronavirus",
abstract="Background: The rise of telehealth and telemedicine during the pandemic allowed patients and providers to develop a sense of comfort with telehealth, which may have increased the demand for virtual-first care solutions with spillover effects into venture capital funding. Objective: We aimed to understand the size and type of digital health investments occurring in the prepandemic and pandemic periods. Methods: We examined health care companies founded from March 14, 2019, to March 14, 2020 (prepandemic) versus those founded from March 15, 2020, to March 14, 2022, after pandemic onset. Data were obtained from Crunchbase, a publicly available database that catalogs information about venture capital investments for companies. We also compared companies founded prepandemic to those founded after the first year of the pandemic (pandemic steady-state). We performed a Wilcoxon rank sum test to compare median funding amounts. We compared the 2 groups of companies according to the type of funding round raised, geography, health care subcategory, total amount of funding per year since founding, and number of founders. Results: There were 2714 and 2218 companies founded prepandemic and during the pandemic, respectively. The companies were similarly distributed across geographies in the prepandemic and pandemic periods (P=.46) with no significant differences in the number of founders (P=.32). There was a significant difference in total funding per year since founding between prepandemic and pandemic companies (US \$10.8 million vs US \$20.9 million; P<.001). The distribution of funding rounds differed significantly for companies founded in prepandemic and pandemic periods (P<.001). On excluding data from the first year of the pandemic, there were 581 companies founded in the pandemic steady-state period from March 14, 2021, to March 14, 2022. Companies founded prepandemic had a significantly greater mean number of founders than those founded during the pandemic (P=.02). There was no significant difference in total funding per year since founding between prepandemic and steady-state pandemic companies (US \$10.8 million vs US \$14.4 million; P=.34). The most common types of health care companies included wellness, biotech/biopharma, and software companies. Distributions of companies across health care subcategories were not significantly different before and during the pandemic. However, significant differences were identified when data from the first year of the pandemic were excluded (P<.001). Companies founded during the steady-state pandemic period were significantly more likely to be classified as artificial intelligence (7.3\% vs 4.7\%; P=.005), software (17.3\% vs 12.7\%; P=.002), and insurance (3.3\% vs 1.7\%; P=.003), and were significantly less likely to be classified as health care diagnostics (2.4\% vs 5.1\%; P=.002). Conclusions: We demonstrate no significant changes in the types of health care companies founded before versus during the pandemic, although significant differences emerge when comparing prepandemic companies to those founded after the first year of the pandemic. ",
doi="10.2196/56327",
url="https://formative.jmir.org/2024/1/e56327"
}


@Article{info:doi/10.2196/41715,
author="Marshall, Jaclyn
and Yurkovic, Alexandra
and Thames, Todd
and Parekh, Ami",
title="A Narrow Definition of Clinical Robustness",
journal="J Med Internet Res",
year="2023",
month="Sep",
day="21",
volume="25",
pages="e41715",
keywords="digital health",
keywords="research",
keywords="virtual care",
doi="10.2196/41715",
url="https://www.jmir.org/2023/1/e41715",
url="http://www.ncbi.nlm.nih.gov/pubmed/37733417"
}


@Article{info:doi/10.2196/48730,
author="Heeres, J. Tjitske
and Tran, Mikael Tri
and Noort, A.C. Bart",
title="Drivers and Barriers to Implementing the Internet of Things in the Health Care Supply Chain: Mixed Methods Multicase Study",
journal="J Med Internet Res",
year="2023",
month="Sep",
day="20",
volume="25",
pages="e48730",
keywords="digital health",
keywords="drivers and barriers",
keywords="healthcare logistics",
keywords="healthcare supply chain",
keywords="implementation",
keywords="Internet of Things",
keywords="supply chain management",
abstract="Background: Over the past 2 years, the COVID-19 pandemic has placed enormous pressure on the health care industry. There has been an increase in demand and, at the same time, a shortage of supplies. This has shown that supply chain management in the health care industry cannot be taken for granted. Furthermore, the health care industry is also facing other major challenges, such as the current labor market shortage. In the literature, the Internet of Things (IoT) is highlighted as an effective tool to build a more resilient and efficient supply chain that can manage these challenges. Although using IoT in supply chain management has been extensively examined in other types of supply chains, its use in the health care supply chain has largely been overlooked. Given that the health care supply chain, compared to others, is more complex and is under growing pressure, a more in-depth understanding of the opportunities brought by IoT is necessary. Objective: This study aims to address this research gap by identifying and ranking the drivers of and barriers to implementing IoT in the health care supply chain. Methods: We conducted a 2-stage study. In the first, exploratory stage, a total of 12 semistructured interviews were conducted to identify drivers and barriers. In the second, confirmatory stage, a total of 26 health care supply chain professionals were asked in a survey to rank the drivers and barriers. Results: The results show that there are multiple financial, operational, strategy-related, and supply chain-related drivers for implementing IoT. Similarly, there are various financial, strategy-related, supply chain-related, technology-related, and user-related barriers. The findings also show that supply chain-related drivers (eg, increased transparency, traceability, and collaboration with suppliers) are the strongest drivers, while financial barriers (eg, high implementation costs and difficulties in building a business case) are the biggest barriers to overcome. Conclusions: The findings of this study add to the limited literature regarding IoT in the health care supply chain by empirically identifying the most important drivers and barriers to IoT implementation. The ranking of drivers and barriers provides guidance for practitioners and health care provider leaders intending to implement IoT in the health care supply chain. ",
doi="10.2196/48730",
url="https://www.jmir.org/2023/1/e48730",
url="http://www.ncbi.nlm.nih.gov/pubmed/37728990"
}


@Article{info:doi/10.2196/43820,
author="Huberty, Jennifer",
title="Real Life Experiences as Head of Science",
journal="JMIR Ment Health",
year="2023",
month="Jan",
day="9",
volume="10",
pages="e43820",
keywords="industry",
keywords="digital health",
keywords="business",
keywords="technology",
keywords="science",
keywords="research",
keywords="CEO",
keywords="founder",
keywords="growth",
doi="10.2196/43820",
url="https://mental.jmir.org/2023/1/e43820",
url="http://www.ncbi.nlm.nih.gov/pubmed/36622751"
}


@Article{info:doi/10.2196/36265,
author="Saunders, Chad
and Currie, Devon
and Virani, Shane
and De Grood, Jill",
title="Navigating the Systemic Conditions of a Digital Health Ecosystem in Alberta, Canada: Embedded Case Study",
journal="JMIR Form Res",
year="2022",
month="Dec",
day="21",
volume="6",
number="12",
pages="e36265",
keywords="digital health",
keywords="entrepreneurial ecosystem",
keywords="systemic conditions",
keywords="policy",
abstract="Background: Digital health promises numerous value-creating outcomes. These include improved health, reduced costs, and the creation of lucrative markets, which, in turn, provide high-quality employment, productivity growth, and a climate that attracts investment. For this value creation and capture, the activities of a diverse set of stakeholders within a digital health ecosystem require coordination. However, the antecedents of the coordination needed for an effective digital health ecosystem are not well understood. Objective: The purpose of this study was to investigate the systemic conditions of the digital health ecosystem in Alberta, Canada, as critical antecedents to ecosystem coordination from the perspective of the authors as applicants to an innovative digital health funding program embedded within the larger digital health ecosystem of innovators or entrepreneurs, health system leaders, support partners, and funders. Methods: We employed a qualitative embedded case study of the systemic conditions within the digital health ecosystem in Alberta, Canada (main case) using semistructured interviews with 36 stakeholders representing innovators or entrepreneurs, health system leaders, support partners, and funders (subcases). The interviews were conducted over a 2-month period between May 26 and July 22, 2021. Data were coded for key themes and synthesized around 5 propositions developed from academic publications and policy reports. Results: The findings indicated varying levels of support for each proposition, with moderate support for accessing real problems, data, training, and space for evaluations. However, the most fundamental gap appears to be in ecosystem navigation, in particular, the absence of intermediaries (eg, individuals, organizations, and technology) to provide guidance on the available support services and dependencies among the various ecosystem actors and programs. Conclusions: Navigating the systemic conditions of the digital health ecosystem is extremely challenging for entrepreneurs, especially those without prior health care experience, and this remains an issue even for those with such experience. Policy interventions aimed at increasing collaboration among ecosystem support providers, along with tools and incentives to ensure coordination, are essential as the ecosystem and those dependent on it grow. ",
doi="10.2196/36265",
url="https://formative.jmir.org/2022/12/e36265",
url="http://www.ncbi.nlm.nih.gov/pubmed/36542428"
}


@Article{info:doi/10.2196/37677,
author="Day, Sean
and Shah, Veeraj
and Kaganoff, Sari
and Powelson, Shannon
and Mathews, C. Simon",
title="Assessing the Clinical Robustness of Digital Health Startups: Cross-sectional Observational Analysis",
journal="J Med Internet Res",
year="2022",
month="Jun",
day="20",
volume="24",
number="6",
pages="e37677",
keywords="digital health",
keywords="health tech",
keywords="software as a medical device (SaaMD)",
keywords="real-world evidence",
keywords="venture capital",
abstract="Background: The digital health sector has experienced rapid growth over the past decade. However, health care technology stakeholders lack a comprehensive understanding of clinical robustness and claims across the industry. Objective: This analysis aimed to examine the clinical robustness and public claims made by digital health companies. Methods: A cross-sectional observational analysis was conducted using company data from the Rock Health Digital Health Venture Funding Database, the US Food and Drug Administration, and the US National Library of Medicine. Companies were included if they sell products targeting the prevention, diagnosis, or treatment phases of the care continuum. Clinical robustness was defined using regulatory filings and clinical trials completed by each company. Public claims data included clinical, economic, and engagement claims regarding product outcomes made by each company on its website. Results: A total of 224 digital health companies with an average age of 7.7 years were included in our cohort. Average clinical robustness was 2.5 (1.8 clinical trials and 0.8 regulatory filings) with a median score of 1. Ninety-eight (44\%) companies had a clinical robustness score of 0, while 45 (20\%) companies had a clinical robustness score of 5 or more. The average number of public claims was 1.3 (0.5 clinical, 0.4 economic, and 0.4 engagement); the median number of claims was 1. No correlation was observed between clinical robustness and number of clinical claims (r2=0.02), clinical robustness and total funding (r2=0.08), or clinical robustness and company age (r2=0.18). Conclusions: Many digital health companies have a low level of clinical robustness and do not make many claims as measured by regulatory filings, clinical trials, and public data shared online. Companies and customers may benefit from investing in greater clinical validation efforts. ",
doi="10.2196/37677",
url="https://www.jmir.org/2022/6/e37677",
url="http://www.ncbi.nlm.nih.gov/pubmed/35723914"
}


@Article{info:doi/10.2196/33128,
author="Velayati, Farnia
and Ayatollahi, Haleh
and Hemmat, Morteza
and Dehghan, Reza",
title="Telehealth Business Models and Their Components: Systematic Review",
journal="J Med Internet Res",
year="2022",
month="Mar",
day="29",
volume="24",
number="3",
pages="e33128",
keywords="telehealth",
keywords="telemedicine",
keywords="mobile health",
keywords="business model",
keywords="value",
keywords="commerce",
keywords="revenue",
keywords="market",
keywords="systematic review",
keywords="health care",
abstract="Background: Telehealth technology is an excellent solution to resolve the problems of health care delivery. However, this technology may fail during large-scale implementation. As a result, business models can be used to facilitate commercialization of telehealth products and services. Objective: The purpose of this study was to review different types of business models or frameworks and their components used in the telehealth industry. Methods: This was a systematic review conducted in 2020. The databases used for searching related articles included Ovid, PubMed, Scopus, Web of Science, Emerald, and ProQuest. Google Scholar was also searched. These databases and Google Scholar were searched until the end of January 2020 and duplicate references were removed. Finally, articles meeting the inclusion criteria were selected and the Critical Appraisal Skills Programme (CASP) checklist was used for appraising the strengths and limitations of each study. Data were extracted using a data extraction form, and the results were synthesized narratively. Results: Initially, 4998 articles were found and after screening, 23 were selected to be included in the study. The results showed that new telehealth business models were presented in 13 studies, and the applications of the existing business models were reported in 10 studies. These studies were related to different types of services, namely, telemonitoring (4 studies), telemedicine (3 studies), mobile health (3 studies), telerehabilitation (3 studies), telehealth (2 studies), assisted living technologies (2 studies), sensor-based systems (2 studies), and mobile teledermoscopy, teleradiology, telecardiology, and teletreatment (1 study related to each area). In most of the business models, value proposition, financial variables, and revenue streams were the main components. Conclusions: Applying business models in the commercialization of telehealth services will be useful to gain a better understanding of the required components, market challenges, and possible future changes. The results showed that different business models can be used for different telehealth technologies in various health systems and cultures. However, it is necessary to evaluate the effectiveness of these models in practice. Moreover, comparing the usefulness of these models in different domains of telehealth services will help identify the strengths and weaknesses of these models for future optimization. ",
doi="10.2196/33128",
url="https://www.jmir.org/2022/3/e33128",
url="http://www.ncbi.nlm.nih.gov/pubmed/35348471"
}


@Article{info:doi/10.2196/27743,
author="G{\"o}rtz, Magdalena
and Byczkowski, Michael
and Rath, Mathias
and Sch{\"u}tz, Viktoria
and Reimold, Philipp
and Gasch, Claudia
and Simpfend{\"o}rfer, Tobias
and M{\"a}rz, Keno
and Seitel, Alexander
and Nolden, Marco
and Ross, Tobias
and Mindroc-Filimon, Diana
and Michael, Dominik
and Metzger, Jasmin
and Onogur, Sinan
and Speidel, Stefanie
and M{\"u}ndermann, Lars
and Fallert, Johannes
and M{\"u}ller, Michael
and von Knebel Doeberitz, Magnus
and Teber, Dogu
and Seitz, Peter
and Maier-Hein, Lena
and Duensing, Stefan
and Hohenfellner, Markus",
title="A Platform and Multisided Market for Translational, Software-Defined Medical Procedures in the Operating Room (OP 4.1): Proof-of-Concept Study",
journal="JMIR Med Inform",
year="2022",
month="Jan",
day="20",
volume="10",
number="1",
pages="e27743",
keywords="cloud-based platform",
keywords="data",
keywords="eHealth",
keywords="Internet of Medical Things",
keywords="IoT",
keywords="medical apps",
keywords="multisided market",
keywords="perioperative medicine",
keywords="software-defined healthcare",
keywords="translational research",
abstract="Background: Although digital and data-based technologies are widespread in various industries in the context of Industry 4.0, the use of smart connected devices in health care is still in its infancy. Innovative solutions for the medical environment are affected by difficult access to medical device data and high barriers to market entry because of proprietary systems. Objective: In the proof-of-concept project OP 4.1, we show the business viability of connecting and augmenting medical devices and data through software add-ons by giving companies a technical and commercial platform for the development, implementation, distribution, and billing of innovative software solutions. Methods: The creation of a central platform prototype requires the collaboration of several independent market contenders, including medical users, software developers, medical device manufacturers, and platform providers. A dedicated consortium of clinical and scientific partners as well as industry partners was set up. Results: We demonstrate the successful development of the prototype of a user-centric, open, and extensible platform for the intelligent support of processes starting with the operating room. By connecting heterogeneous data sources and medical devices from different manufacturers and making them accessible for software developers and medical users, the cloud-based platform OP 4.1 enables the augmentation of medical devices and procedures through software-based solutions. The platform also allows for the demand-oriented billing of apps and medical devices, thus permitting software-based solutions to fast-track their economic development and become commercially successful. Conclusions: The technology and business platform OP 4.1 creates a multisided market for the successful development, implementation, distribution, and billing of new software solutions in the operating room and in the health care sector in general. Consequently, software-based medical innovation can be translated into clinical routine quickly, efficiently, and cost-effectively, optimizing the treatment of patients through smartly assisted procedures. ",
doi="10.2196/27743",
url="https://medinform.jmir.org/2022/1/e27743",
url="http://www.ncbi.nlm.nih.gov/pubmed/35049510"
}


@Article{info:doi/10.2196/33600,
author="Zhang, Zhongan
and Zheng, Xu
and An, Kai
and He, Yunfan
and Wang, Tong
and Zhou, Ruizhu
and Zheng, Qilin
and Nuo, Mingfu
and Liang, Jun
and Lei, Jianbo",
title="Current Status of the Health Information Technology Industry in China from the China Hospital Information Network Conference: Cross-sectional Study of Participating Companies",
journal="JMIR Med Inform",
year="2022",
month="Jan",
day="11",
volume="10",
number="1",
pages="e33600",
keywords="medical informatics",
keywords="China Hospital Information Network Conference",
keywords="industry analysis",
keywords="county medical community",
keywords="smart hospital",
keywords="cross-sectional study",
keywords="digital therapeutic",
keywords="information network",
keywords="health care",
keywords="hospital information",
keywords="medical information",
keywords="tertiary hospital",
abstract="Background: The China Hospital Information Network Conference (CHINC) is one of the most influential academic and technical exchange activities in medical informatics and medical informatization in China. It collects frontier ideas in medical information and has an important reference value for the analysis of China's medical information industry development. Objective: This study summarizes the current situation and future development of China's medical information industry and provides a future reference for China and abroad in the future by analyzing the characteristics of CHINC exhibitors in 2021. Methods: The list of enterprises and participating keywords were obtained from the official website of CHINC. Basic characteristics of the enterprises, industrial fields, applied technologies, company concepts, and other information were collected from the TianYanCha website and the VBDATA company library. Descriptive analysis was used to analyze the collected data, and we summarized the future development directions. Results: A total of 205 enterprises officially participated in the exhibition. Most of the enterprises were newly founded, of which 61.9\% (127/205) were founded in the past 10 years. The majority of these enterprises were from first-tier cities, and 79.02\% (162/205) were from Beijing, Zhejiang, Guangdong, Shanghai, and Jiangsu Provinces. The median registered capital is 16.67 million RMB (about US \$2.61 million), and there are 35 (72.2\%) enterprises with a registered capital of more than 100 million RMB (about US \$15.68 million), 17 (8.3\%) of which are already listed. A total of 126 enterprises were found in the VBDATA company library, of which 39 (30.9\%) are information technology vendors and 57 (45.2\%) are application technology vendors. In addition, 16 of the 57 (28\%) use artificial intelligence technology. Smart medicine and internet hospitals were the focus of the enterprises participating in this conference. Conclusions: China's tertiary hospital informatization has basically completed the construction of the primary stage. The average grade of hospital electronic medical records exceeds grade 3, and 78.13\% of the provinces have reached grade 3 or above. The characteristics are as follows: On the one hand, China's medical information industry is focusing on the construction of smart hospitals, including intelligent systems supporting doctors' scientific research, diagnosis-related group intelligent operation systems, and office automation systems supporting hospital management, single-disease clinical decision support systems assisting doctors' clinical care, and intelligent internet of things for logistics. On the other hand, the construction of a compact county medical community is becoming a new focus of enterprises under the guidance of practical needs and national policies to improve the quality of grassroots health services. In addition, whole-course management and digital therapy will also become a new hotspot in the future. ",
doi="10.2196/33600",
url="https://medinform.jmir.org/2022/1/e33600",
url="http://www.ncbi.nlm.nih.gov/pubmed/35014959"
}


@Article{info:doi/10.2196/32446,
author="Kikuchi, Satoru
and Kodama, Kota
and Sengoku, Shintaro",
title="The Significance of Alliance Networks in Research and Development of Digital Health Products for Diabetes: Observational Study",
journal="JMIR Diabetes",
year="2021",
month="Oct",
day="21",
volume="6",
number="4",
pages="e32446",
keywords="digital health",
keywords="alliance",
keywords="network",
keywords="wearable device",
keywords="diabetes",
abstract="Background: Digital health has been advancing owing to technological progress by means of smart devices and artificial intelligence, among other developments. In the field of diabetes especially, there are many active use cases of digital technology supporting the treatment of diabetes and improving lifestyle. In the innovation ecosystem, new alliance networks are formed not only by medical device companies and pharmaceutical companies, but also by information and communications technology companies and start-ups. While understanding and utilizing the network structure is important to increase the competitive advantage of companies, there is a lack of previous research describing the structure of alliance networks and the factors that lead to their formation in digital health. Objective: The aim of this study was to explore the significance of alliance networks, focusing on digital health for diabetes, in effectively implementing processes, from the research and development of products or services to their launch and market penetration. Methods: First, we listed the companies and contracts related to digital health for diabetes, visualized the change in the number of companies and the connections between companies in each industry, and analyzed the overview of the network. Second, we calculated the degree, betweenness centrality, and eigenvector centrality of each company in each year. Next, we analyzed the relationship between network centrality and market competitiveness by using annual sales as a parameter of company competitiveness. We also compared the network centrality of each company by industry or headquarters location (or both) and analyzed the characteristics of companies with higher centrality. Finally, we analyzed the relationship between network centrality and the number of products certified or approved by the US Food and Drug Administration. Results: We found the degree centrality of companies was correlated with an increase in their sales. The betweenness and eigenvector centralities of medical devices companies located in the United States were significantly higher than those outside the United States (P=.04 and .005, respectively). Finally, the degree, betweenness, and eigenvector centralities were correlated with an increase in the number of Class III, but not of Class I nor II, medical device products. Conclusions: These findings give rise to new insights into industry ecosystem for digital health and its requirement and expect a contribution to research and development practices in the field of digital health. ",
doi="10.2196/32446",
url="https://diabetes.jmir.org/2021/4/e32446",
url="http://www.ncbi.nlm.nih.gov/pubmed/34673525"
}


@Article{info:doi/10.2196/24890,
author="Lyles, Rees Courtney
and Adler-Milstein, Julia
and Thao, Crishyashi
and Lisker, Sarah
and Nouri, Sarah
and Sarkar, Urmimala",
title="Alignment of Key Stakeholders' Priorities for Patient-Facing Tools in Digital Health: Mixed Methods Study",
journal="J Med Internet Res",
year="2021",
month="Aug",
day="26",
volume="23",
number="8",
pages="e24890",
keywords="medical informatics",
keywords="medical informatics apps",
keywords="information technology",
keywords="implementation science",
keywords="mixed methods",
abstract="Background: There is widespread agreement on the promise of patient-facing digital health tools to transform health care. Yet, few tools are in widespread use or have documented clinical effectiveness. Objective: The aim of this study was to gain insight into the gap between the potential of patient-facing digital health tools and real-world uptake. Methods: We interviewed and surveyed experts (in total, n=24) across key digital health stakeholder groups---venture capitalists, digital health companies, payers, and health care system providers or leaders---guided by the Consolidated Framework for Implementation Research. Results: Our findings revealed that external policy, regulatory demands, internal organizational workflow, and integration needs often take priority over patient needs and patient preferences for digital health tools, which lowers patient acceptance rates. We discovered alignment, across all 4 stakeholder groups, in the desire to engage both patients and frontline health care providers in broader dissemination and evaluation of digital health tools. However, major areas of misalignment between stakeholder groups have stymied the progress of digital health tool uptake---venture capitalists and companies focused on external policy and regulatory demands, while payers and providers focused on internal organizational workflow and integration needs. Conclusions: Misalignment of the priorities of digital health companies and their funders with those of providers and payers requires direct attention to improve uptake of patient-facing digital health tools and platforms. ",
doi="10.2196/24890",
url="https://www.jmir.org/2021/8/e24890",
url="http://www.ncbi.nlm.nih.gov/pubmed/34435966"
}


@Article{info:doi/10.2196/29170,
author="Chiu, Ya-Ling
and Wang, Jying-Nan
and Yu, Haiyan
and Hsu, Yuan-Teng",
title="Consultation Pricing of the Online Health Care Service in China: Hierarchical Linear Regression Approach",
journal="J Med Internet Res",
year="2021",
month="Jul",
day="14",
volume="23",
number="7",
pages="e29170",
keywords="online health care industry",
keywords="consulting pricing",
keywords="reputation",
keywords="wage level",
keywords="hierarchical linear modeling",
keywords="modeling",
keywords="online consultation",
keywords="pricing",
keywords="linear regression",
keywords="consultation",
keywords="physician",
keywords="eHealth",
abstract="Background: Online health care services are a possible solution to alleviate the lack of medical resources in rural areas, and further understanding of the related medical service pricing system would contribute to improvement of the online health care community (OHC). Although many studies have investigated the OHC, the impact of physicians' reputations and wage levels on consulting prices in the OHC has rarely been discussed in the literature. Objective: This study was designed to explore the determinants of consulting prices in the OHC. We addressed the following questions: (1) Are the prices of online health consultation services affected by wage levels at the doctor's location? (2) How does a physician's online and offline reputation affect their consulting prices? Methods: Employing a large-scale sample of 16,008 doctors in China, we first used descriptive statistics to investigate the determinants of consulting prices in their entirety. Hierarchical linear modeling was then used to investigate the determinants of consulting prices in the OHC. Results: The empirical results led to the conclusion that if doctors have more elevated clinic titles, work in higher-level hospitals, have better online reputations, and/or have made more past sales, their consulting prices will be higher. Additionally, the wage level in the city in which the doctor is working determines their opportunity cost and therefore also affects consulting prices. Conclusions: The findings indicate that the characteristics of the doctor, the doctor's online reputation, and past sales affect the consulting price. In particular, the wage level in the city affects the price of the consultation. These findings highlight that the OHC is important because it can indeed break through geographical restrictions and give rural residents the opportunity to obtain medical service from doctors in big cities. However, doctors from cities often charge higher fees because of their higher opportunity cost. The results reveal that one of the most important functions of the OHC is to reduce the medical disparity between urban and rural areas; however, planners appear to ignore the possibility that rural residents with lower incomes may not be able to afford such high medical consultation costs. Therefore, the government should consider providing incentives to encourage urban doctors to provide discounts to rural residents or directly offer appropriate subsidies. ",
doi="10.2196/29170",
url="https://www.jmir.org/2021/7/e29170",
url="http://www.ncbi.nlm.nih.gov/pubmed/34259643"
}


@Article{info:doi/10.2196/24724,
author="Christie, Liane Hannah
and Boots, Maria Lizzy Mitzy
and Hermans, Ivo
and Govers, Mark
and Tange, Johannes Huibert
and Verhey, Josef Frans Rochus
and de Vugt, Majolein",
title="Business Models of eHealth Interventions to Support Informal Caregivers of People With Dementia in the Netherlands: Analysis of Case Studies",
journal="JMIR Aging",
year="2021",
month="Jun",
day="3",
volume="4",
number="2",
pages="e24724",
keywords="eHealth",
keywords="dementia",
keywords="caregiving",
keywords="implementation",
keywords="business modeling",
abstract="Background: In academic research contexts, eHealth interventions for caregivers of people with dementia have shown ample evidence of effectiveness. However, they are rarely implemented in practice, and much can be learned from their counterparts (commercial, governmental, or other origins) that are already being used in practice. Objective: This study aims to examine a sample of case studies of eHealth interventions to support informal caregivers of people with dementia that are currently used in the Netherlands; to investigate what strategies are used to ensure the desirability, feasibility, viability, and sustainability of the interventions; and to apply the lessons learned from this practical, commercial implementation perspective to academically developed eHealth interventions for caregivers of people with dementia. Methods: In step 1, experts (N=483) in the fields of dementia and eHealth were contacted and asked to recommend interventions that met the following criteria: delivered via the internet; suitable for informal caregivers of people with dementia; accessible in the Netherlands, either in Dutch or in English; and used in practice. The contacted experts were academics working on dementia and psychosocial innovations, industry professionals from eHealth software companies, clinicians, patient organizations, and people with dementia and their caregivers. In step 2, contact persons from the suggested eHealth interventions participated in a semistructured telephone interview. The results were analyzed using a multiple case study methodology. Results: In total, the response rate was 7.5\% (36/483), and 21 eHealth interventions for caregivers of people with dementia were recommended. Furthermore, 43\% (9/21) of the interventions met all 4 criteria and were included in the sample for the case study analysis. Of these 9 interventions, 4 were found to have developed sustainable business models and 5 were implemented in a more exploratory manner and relied on research grants to varying extents, although some had also developed preliminary business models. Conclusions: These findings suggest that the desirability, feasibility, and viability of eHealth interventions for caregivers of people with dementia are linked to their integration into larger structures, their ownership and support of content internally, their development of information and communication technology services externally, and their offer of fixed, low pricing. The origin of the case studies was also important, as eHealth interventions that had originated in an academic research context less reliably found their way to sustainable implementation. In addition, careful selection of digital transformation strategies, more intersectoral cooperation, and more funding for implementation and business modeling research are recommended to help future developers bring eHealth interventions for caregivers of people with dementia into practice. ",
doi="10.2196/24724",
url="https://aging.jmir.org/2021/2/e24724",
url="http://www.ncbi.nlm.nih.gov/pubmed/34081009"
}


@Article{info:doi/10.2196/19079,
author="Tam, K. Emily
and Dong, Xuezhi",
title="Survey of Residency Directors' Views on Entrepreneurship",
journal="JMIR Med Educ",
year="2021",
month="Apr",
day="14",
volume="7",
number="2",
pages="e19079",
keywords="medical student education",
keywords="medical student innovation",
keywords="health innovation",
keywords="program director",
doi="10.2196/19079",
url="https://mededu.jmir.org/2021/2/e19079",
url="http://www.ncbi.nlm.nih.gov/pubmed/33851929"
}


@Article{info:doi/10.2196/23654,
author="Lai, Claudia
and Deber, Raisa
and Jadad, R. Alejandro
and Shachak, Aviv",
title="Income-Generating Processes of Free Web-Based Digital Health Tools to Engage Patients: Qualitative Analysis",
journal="J Med Internet Res",
year="2021",
month="Feb",
day="3",
volume="23",
number="2",
pages="e23654",
keywords="digital health",
keywords="patient engagement",
keywords="eHealth",
keywords="health information",
abstract="Background: In recent years, digital tools have become a viable means for patients to address their health and information needs. Governments and health care organizations are offering digital tools such as self-assessment tools, symptom tracking tools, or chatbots. Other sources of digital tools, such as those offered through patient platforms, are available on the internet free of charge. We define patient platforms as health-specific websites that offer tools to anyone with internet access to engage them in their health care process with peer networks to support their learning. Although numerous social media platforms engage users without up-front charges, patient platforms are specific to health. As little is known about their business model, there is a need to understand what else these platforms are trying to achieve beyond supporting patients so that patients can make informed decisions about the benefits and risks of using the digital tools they offer. Objective: The aim of this study is to explore what patient platforms are trying to achieve beyond supporting patients and how their digital tools can be used to generate income. Methods: Textual and visual data collected from a purposeful selection of 11 patient platforms from September 2013 to August 2014 were analyzed using framework analysis. Data were systematically and rigorously coded and categorized according to key issues and themes by following 5 steps: familiarizing, identifying a thematic framework, indexing, charting, and mapping and interpretation. We used open coding to identify additional concepts not captured in the initial thematic framework. This paper reports on emergent findings on the business models of the platforms and their income-generating processes. Results: Our analysis revealed that in addition to patients, the platforms support other parties with interests in health and information exchanges. Patient platforms did not charge up-front fees but generated income from other sources, such as advertising, sponsorship, marketing (eg, sending information to users on behalf of sponsors or providing means for sponsors to reach patients directly), supporting other portals, and providing research services. Conclusions: This study reports on the mechanisms by which some patient platforms generate income to support their operations, gain profit, or both. Although income-generating processes exist elsewhere on social media platforms in general, they pose unique challenges in the health context because digital tools engage patients in health and information exchanges. This study highlights the need to minimize the potential for unintended consequences that can pose health risks to patients or can lead to increased health expenses. By understanding other interests that patient platforms support, our findings point to important policy implications, such as whether (and how) authorities might protect users from processes that may not always be in their best interests and can potentially incur costs to the health system. ",
doi="10.2196/23654",
url="http://www.jmir.org/2021/2/e23654/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33533722"
}


@Article{info:doi/10.2196/20579,
author="Kelley, Taylor Leah
and Fujioka, Jamie
and Liang, Kyle
and Cooper, Madeline
and Jamieson, Trevor
and Desveaux, Laura",
title="Barriers to Creating Scalable Business Models for Digital Health Innovation in Public Systems: Qualitative Case Study",
journal="JMIR Public Health Surveill",
year="2020",
month="Dec",
day="10",
volume="6",
number="4",
pages="e20579",
keywords="digital technologies",
keywords="telemedicine",
keywords="innovation diffusion",
keywords="health policy",
keywords="evaluation study",
keywords="reimbursement",
keywords="incentive",
keywords="mobile phone",
abstract="Background: Health systems are increasingly looking toward the private sector to provide digital solutions to address health care demands. Innovation in digital health is largely driven by small- and medium-sized enterprises (SMEs), yet these companies experience significant barriers to entry, especially in public health systems. Complex and fragmented care models, alongside a myriad of relevant stakeholders (eg, purchasers, providers, and producers of health care products), make developing value propositions for digital solutions highly challenging. Objective: This study aims to identify areas for health system improvement to promote the integration of innovative digital health technologies developed by SMEs. Methods: This paper qualitatively analyzes a series of case studies to identify health system barriers faced by SMEs developing digital health technologies in Canada and proposed solutions to encourage a more innovative ecosystem. The Women's College Hospital Institute for Health System Solutions and Virtual Care established a consultation program for SMEs to help them increase their innovation capacity and take their ideas to market. The consultation involved the SME filling out an onboarding form and review of this information by an expert advisory committee using guided considerations, leading to a recommendation report provided to the SME. This paper reports on the characteristics of 25 SMEs who completed the program and qualitatively analyzed their recommendation reports to identify common barriers to digital health innovation. Results: A total of 2 central themes were identified, each with 3 subthemes. First, a common barrier to system integration was the lack of formal evaluation, with SMEs having limited resources and opportunities to conduct such an evaluation. Second, the health system's current structure does not create incentives for clinicians to use digital technologies, which threatens the sustainability of SMEs' business models. SMEs faced significant challenges in engaging users and payers from the public system due to perverse economic incentives. Physicians are compensated by in-person visits, which actively works against the goals of many digital health solutions of keeping patients out of clinics and hospitals. Conclusions: There is a significant disconnect between the economic incentives that drive clinical behaviors and the use of digital technologies that would benefit patients' well-being. To encourage the use of digital health technologies, publicly funded health systems need to dedicate funding for the evaluation of digital solutions and streamlined pathways for clinical integration. ",
doi="10.2196/20579",
url="http://publichealth.jmir.org/2020/4/e20579/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33300882"
}


@Article{info:doi/10.2196/19644,
author="Cresswell, Kathrin
and Williams, Robin
and Carlile, Narath
and Sheikh, Aziz",
title="Accelerating Innovation in Health Care: Insights From a Qualitative Inquiry Into United Kingdom and United States Innovation Centers",
journal="J Med Internet Res",
year="2020",
month="Sep",
day="25",
volume="22",
number="9",
pages="e19644",
keywords="innovation",
keywords="health information technology",
keywords="health care",
abstract="Background: Digital health innovations are being prioritized on international policy agendas in the hope that they will help to address the existing health system challenges. Objective: The aim of this study was to explore the setup, design, facilities, and strategic priorities of leading United Kingdom and United States health care innovation centers to identify transferable lessons for accelerating their creation and maximizing their impact. Methods: We conducted qualitative case studies consisting of semistructured, audio-recorded interviews with decision makers and center staff in 6 innovation centers. We also conducted nonparticipant observations of meetings and center tours, where we took field notes. Qualitative data were analyzed initially within and then across cases facilitated by QSR International's NVivo software. Results: The centers had different institutional arrangements, including university-associated institutes or innovation laboratories, business accelerators or incubators, and academic health science partnership models. We conducted interviews with 34 individuals, 1 group interview with 3 participants, and observations of  4 meetings. Although the centers differed significantly in relation to their mission, structure, and governance, we observed key common characteristics. These included high-level leadership support and incentives to engage in innovation activities, a clear mission to address identified gaps within their respective organizational and health system settings, physical spaces that facilitated networking through open-door policies, flat managerial structures characterized by new organizational roles for which boundary spanning was key, and a wider innovation ecosystem that was strategically and proactively engaged with the center facilitating external partnerships. Conclusions: Although innovation in health care settings is unpredictable, we offer insights that may help those establishing innovation centers. The key in this respect is the ability to support different kinds of innovations at different stages through adequate support structures, including the development of new career pathways. ",
doi="10.2196/19644",
url="http://www.jmir.org/2020/9/e19644/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32975524"
}


@Article{info:doi/10.2196/19480,
author="Chang, Ernest Shuchih
and Chen, YiChian",
title="Blockchain in Health Care Innovation: Literature Review and Case Study From a Business Ecosystem Perspective",
journal="J Med Internet Res",
year="2020",
month="Aug",
day="31",
volume="22",
number="8",
pages="e19480",
keywords="blockchain",
keywords="health care industry",
keywords="business ecosystem",
keywords="smart contract",
keywords="paradigm shift",
abstract="Background: Blockchain technology is leveraging its innovative potential in various sectors and its transformation of business-related processes has drawn much attention. Topics of research interest have focused on medical and health care applications, while research implications have generally concluded in system design, literature reviews, and case studies. However, a general overview and knowledge about the impact on the health care ecosystem is limited. Objective: This paper explores a potential paradigm shift and ecosystem evolution in health care utilizing blockchain technology. Methods: A literature review with a case study on a pioneering initiative was conducted. With a systematic life cycle analysis, this study sheds light on the evolutionary development of blockchain in health care scenarios and its interactive relationship among stakeholders. Results: Four stages---birth, expansion, leadership, and self-renewal or death---in the life cycle of the business ecosystem were explored to elucidate the evolving trajectories of blockchain-based health care implementation. Focused impacts on the traditional health care industry are highlighted within each stage to further support the potential health care paradigm shift in the future. Conclusions: This paper enriches the existing body of literature in this field by illustrating the potential of blockchain in fulfilling stakeholders' needs and elucidating the phenomenon of coevolution within the health care ecosystem. Blockchain not only catalyzes the interactions among players but also facilitates the formation of the ecosystem life cycle. The collaborative network linked by blockchain may play a critical role on value creation, transfer, and sharing among the health care community. Future efforts may focus on empirical or case studies to validate the proposed evolution of the health care ecosystem. ",
doi="10.2196/19480",
url="http://www.jmir.org/2020/8/e19480/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32865501"
}


@Article{info:doi/10.2196/17272,
author="Lupi{\'a}{\~n}ez-Villanueva, Francisco
and Folkvord, Frans
and Vanden Abeele, Mariek",
title="Influence of the Business Revenue, Recommendation, and Provider Models on Mobile Health App Adoption: Three-Country Experimental Vignette Study",
journal="JMIR Mhealth Uhealth",
year="2020",
month="Jun",
day="4",
volume="8",
number="6",
pages="e17272",
keywords="mHealth adoption",
keywords="experiment",
keywords="mobile apps",
keywords="self-monitoring",
keywords="privacy paradox",
keywords="business model",
keywords="data protection",
keywords="recommendation",
keywords="health consciousness",
keywords="health information orientation",
keywords="eHealth literacy",
abstract="Background: Despite the worldwide growth in mobile health (mHealth) tools and the possible benefits of mHealth for patients and health care providers, scientific research examining factors explaining the adoption level of mHealth tools remains scarce. Objective: We performed an experimental vignette study to investigate how four factors related to the business model of an mHealth app affect its adoption and users' willingness to pay: (1) the revenue model (ie, sharing data with third parties vs accepting advertisements); (2) the data protection model (General Data Protection Regulation [GDPR]-compliant data handling vs nonGDPR-compliant data handling); (3) the recommendation model (ie, doctor vs patient recommendation); and (4) the provider model (ie, pharmaceutical vs medical association provider). In addition, health consciousness, health information orientation, and electronic health literacy were explored as intrapersonal predictors of adoption. Methods: We conducted an experimental study in three countries, Spain (N=800), Germany (N=800), and the Netherlands (N=416), to assess the influence of multiple business models and intrapersonal characteristics on the willingness to pay and intention to download a health app. Results: The revenue model did not affect willingness to pay or intentions to download the app in all three countries. In the Netherlands, data protection increased willingness to pay for the health app (P<.001). Moreover, in all three countries, data protection increased the likelihood of downloading the app (P<.001). In Germany (P=.04) and the Netherlands (P=.007), a doctor recommendation increased both willingness to pay and intention to download the health app. For all three countries, apps manufactured in association with a medical organization were more likely to be downloaded (P<.001). Finally, in all three countries, men, younger individuals, those with higher levels of education, and people with a health information orientation were willing to pay more for adoption of the health app and had a higher intention to download the app. Conclusions: The finding that people want their data protected by legislation but are not willing to pay more for data protection suggests that in the context of mHealth, app privacy protection cannot be leveraged as a selling point. However, people do value a doctor recommendation and apps manufactured by a medical association, which particularly influence their intention to download an mHealth app. ",
doi="10.2196/17272",
url="https://mhealth.jmir.org/2020/6/e17272",
url="http://www.ncbi.nlm.nih.gov/pubmed/32496204"
}


@Article{info:doi/10.2196/17004,
author="Poncette, Akira-Sebastian
and Rojas, Pablo-David
and Hofferbert, Joscha
and Valera Sosa, Alvaro
and Balzer, Felix
and Braune, Katarina",
title="Hackathons as Stepping Stones in Health Care Innovation: Case Study With Systematic Recommendations",
journal="J Med Internet Res",
year="2020",
month="Mar",
day="24",
volume="22",
number="3",
pages="e17004",
keywords="digital health",
keywords="transdisciplinary research",
keywords="hackathon",
keywords="technological innovation",
keywords="patient-centered care",
keywords="social media",
abstract="Background: Until recently, developing health technologies was time-consuming and expensive, and often involved patients, doctors, and other health care professionals only as passive recipients of the end product. So far, users have been minimally involved in the ideation and creation stages of digital health technologies. In order to best address users' unmet needs, a transdisciplinary and user-led approach, involving cocreation and direct user feedback, is required. In this context, hackathon events have become increasingly popular in generating enthusiasm for user-centered innovation. Objective: This case study describes preparatory steps and the performance of a health hackathon directly involving patients and health care professionals at all stages. Feasibility and outcomes were assessed, leading to the development of systematic recommendations for future hackathons as a vehicle for bottom-up innovation in health care. Methods: A 2-day hackathon was conducted in February 2017 in Berlin, Germany. Data were collected through a field study. Collected field notes were subsequently discussed in 15 informal meetings among the research team. Experiences of conducting two further hackathons in December 2017 and November 2018 were included. Results: In total, 30 participants took part, with 63\% (19/30) of participants between 25 and 34 years of age, 30\% (9/30) between 35 and 44 years of age, and 7\% (2/30) younger than 25 years of age. A total of 43\% (13/30) of the participants were female. The participation rate of medical experts, including patients and health care professionals, was 30\% (9/30). Five multidisciplinary teams were formed and each tackled a specific health care problem. All presented projects were apps: a chatbot for skin cancer recognition, an augmented reality exposure-based therapy (eg, for arachnophobia), an app for medical neighborhood connectivity, a doctor appointment platform, and a self-care app for people suffering from depression. Patients and health care professionals initiated all of the projects. Conducting the hackathon resulted in significant growth of the digital health community of Berlin and was followed up by larger hackathons. Systematic recommendations for conducting cost-efficient hackathons (n?30) were developed, including aspects of community building, stakeholder engagement, mentoring, themes, announcements, follow-up, and timing for each step. Conclusions: This study shows that hackathons are effective in bringing innovation to health care and are more cost- and time-efficient and potentially more sustainable than traditional medical device and digital product development. Our systematic recommendations can be useful to other individuals and organizations that want to establish user-led innovation in academic hospitals by conducting transdisciplinary hackathons. ",
doi="10.2196/17004",
url="http://www.jmir.org/2020/3/e17004/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32207691"
}


@Article{info:doi/10.2196/14890,
author="Thiebes, Scott
and Toussaint, A. Philipp
and Ju, Jaehyeon
and Ahn, Jae-Hyeon
and Lyytinen, Kalle
and Sunyaev, Ali",
title="Valuable Genomes: Taxonomy and Archetypes of Business Models in Direct-to-Consumer Genetic Testing",
journal="J Med Internet Res",
year="2020",
month="Jan",
day="21",
volume="22",
number="1",
pages="e14890",
keywords="genomics",
keywords="genetic testing",
keywords="genetic privacy",
keywords="direct-to-consumer screening and testing",
keywords="taxonomy",
keywords="cluster analysis",
abstract="Background: Recent progress in genome data collection and analysis technologies has led to a surge of direct-to-consumer (DTC) genetic testing services. Owing to the clinical value and sensitivity of genomic data, as well as uncertainty and hearsay surrounding business practices of DTC genetic testing service providers, DTC genetic testing has faced significant criticism by researchers and practitioners. Research in this area has centered on ethical and legal implications of providing genetic tests directly to consumers, but we still lack a more profound understanding of how businesses in the DTC genetic testing markets work and provide value to different stakeholders. Objective: The aim of this study was to address the lack of knowledge concerning business models of DTC genetic testing services by systematically identifying the salient properties of various DTC genetic testing service business models as well as discerning dominant business models in the market. Methods: We employed a 3-phased research approach. In phase 1, we set up a database of 277 DTC genetic testing services. In phase 2, we drew on these data as well as conceptual models of DTC genetic testing services and iteratively developed a taxonomy of DTC genetic testing service business models. In phase 3, we used a 2-stage clustering method to cluster the 277 services that we identified during phase 1 and derived 6 dominant archetypes of DTC genetic testing service business models. Results: The contributions of this research are 2-fold. First, we provided a first of its kind, systematically developed taxonomy of DTC genetic testing service business models consisting of 15 dimensions in 4 categories. Each dimension comprises 2 to 5 characteristics and captures relevant aspects of DTC genetic testing service business models. Second, we derived 6 archetypes of DTC genetic testing service business models named as follows: (1) low-cost DTC genomics for enthusiasts, (2) high-privacy DTC genomics for enthusiasts, (3) specific information tests, (4) simple health tests, (5) basic low-value DTC genomics, and (6) comprehensive tests and low data processing. Conclusions: Our analysis paints a much more complex business landscape in the DTC genetic testing market than previously anticipated. This calls for further research on business models and their effects that underlie DTC genetic testing services and invites specific regulatory interventions to protect consumers and level the playing field. ",
doi="10.2196/14890",
url="https://www.jmir.org/2020/1/e14890",
url="http://www.ncbi.nlm.nih.gov/pubmed/31961329"
}


@Article{info:doi/10.2196/14304,
author="Sterling, Ryan
and LeRouge, Cynthia",
title="On-Demand Telemedicine as a Disruptive Health Technology: Qualitative Study Exploring Emerging Business Models and Strategies Among Early Adopter Organizations in the United States",
journal="J Med Internet Res",
year="2019",
month="Nov",
day="15",
volume="21",
number="11",
pages="e14304",
keywords="telemedicine",
keywords="disruptive technology",
keywords="business model",
keywords="business strategy",
abstract="Background: On-demand telemedicine is increasingly adopted by health organizations to meet patient demand for convenient, accessible, and affordable services. Little guidance is currently available to new entrant organizations as they consider viable business models and strategies to harness the disruptive potential of on-demand telemedicine services (in particular, virtual urgent care clinics [VCCs] as a predominant and catalyst form of on-demand telemedicine). Objective: We recognized on-demand telemedicine as a disruptive technology to explore the experiences of early adopter organizations as they launch on-demand telemedicine services and deploy business models and strategies. Focusing on VCC service lines, this study addressed the following research questions: (1) what is the emerging business model being deployed for on-demand telemedicine?; (2) what are the core components of the emerging business model for on-demand telemedicine?; and (3) what are the disruptive business strategies employed by early adopter organizations as they launch on-demand telemedicine services? Methods: This qualitative study gathered data from 32 semistructured phone interviews with key informants from 19 VCC early adopter organizations across the United States. Interview protocols were developed based on noted dissemination and implementation science frameworks. We used the constant comparison method to transform study data into stable dimensions that revealed emerging business models, core business model components (value proposition, key resources, key processes, and profit formula), and accompanying business strategies. Results: Early adopters are deploying business models that most closely align with a value-adding process model archetype. By and large, we found that this general model appropriately matches resources, processes, and profit formulas to support the disruptive potential of on-demand telemedicine. In total, 4 business strategy areas were discovered to particularly contribute to business model success for on-demand disruption among early adopters: fundamental disruptions to the model of care delivery; outsourcing support for on-demand services; disruptive market strategies to target potential users; and new and unexpected organizational partnerships to increase return on investment. Conclusions: On-demand telemedicine is a potentially disruptive innovation currently in the early adopter stage of technology adoption and diffusion. On-demand telemedicine must cross into the early majority stage to truly be a positive disruption that will increase accessibility and affordability for health care consumers. Our findings provide guidance for adopter organizations as they seek to deploy viable business models and successful strategies to smooth the transition to early majority status. We present important insights for both early adopters and potential early majority organizations to better harness the disruptive potential of on-demand telemedicine. ",
doi="10.2196/14304",
url="http://www.jmir.org/2019/11/e14304/",
url="http://www.ncbi.nlm.nih.gov/pubmed/31730038"
}


@Article{info:doi/10.2196/jmir.9498,
author="Herrmann, Maximilian
and Boehme, Philip
and Mondritzki, Thomas
and Ehlers, P. Jan
and Kavadias, Stylianos
and Truebel, Hubert",
title="Digital Transformation and Disruption of the Health Care Sector: Internet-Based Observational Study",
journal="J Med Internet Res",
year="2018",
month="Mar",
day="27",
volume="20",
number="3",
pages="e104",
keywords="digital transformation",
keywords="health care sector",
keywords="health care reform",
keywords="incremental innovation",
keywords="disruptive innovation",
keywords="organizational innovation",
keywords="entrepreneurship",
keywords="efficiency",
keywords="models",
keywords="organizational",
keywords="diffusion of innovation",
keywords="delivery of health care",
abstract="Background: Digital innovation, introduced across many industries, is a strong force of transformation. Some industries have seen faster transformation, whereas the health care sector only recently came into focus. A context where digital corporations move into health care, payers strive to keep rising costs at bay, and longer-living patients desire continuously improved quality of care points to a digital and value-based transformation with drastic implications for the health care sector. Objective: We tried to operationalize the discussion within the health care sector around digital and disruptive innovation to identify what type of technological enablers, business models, and value networks seem to be emerging from different groups of innovators with respect to their digital transformational efforts. Methods: From the Forbes 2000 and CBinsights databases, we identified 100 leading technology, life science, and start-up companies active in the health care sector. Further analysis identified projects from these companies within a digital context that were subsequently evaluated using the following criteria: delivery of patient value, presence of a comprehensive and distinctive underlying business model, solutions provided, and customer needs addressed. Results: Our methodological approach recorded more than 400 projects and collaborations. We identified patterns that show established corporations rely more on incremental innovation that supports their current business models, while start-ups engage their flexibility to explore new market segments with notable transformations of established business models. Thereby, start-ups offer higher promises of disruptive innovation. Additionally, start-ups offer more diversified value propositions addressing broader areas of the health care sector. Conclusions: Digital transformation is an opportunity to accelerate health care performance by lowering cost and improving quality of care. At an economic scale, business models can be strengthened and disruptive innovation models enabled. Corporations should look for collaborations with start-up companies to keep investment costs at bay and off the balance sheet. At the same time, the regulatory knowledge of established corporations might help start-ups to kick off digital disruption in the health care sector. ",
doi="10.2196/jmir.9498",
url="http://www.jmir.org/2018/3/e104/",
url="http://www.ncbi.nlm.nih.gov/pubmed/29588274"
}


@Article{info:doi/10.2196/jmir.5244,
author="Roettl, Johanna
and Bidmon, Sonja
and Terlutter, Ralf",
title="What Predicts Patients' Willingness to Undergo Online Treatment and Pay for Online Treatment? Results from a Web-Based Survey to Investigate the Changing Patient-Physician Relationship",
journal="J Med Internet Res",
year="2016",
month="Feb",
day="04",
volume="18",
number="2",
pages="e32",
keywords="physician-patient relationship, online treatment",
keywords="general practitioners",
keywords="willingness to pay",
abstract="Background: Substantial research has focused on patients' health information--seeking behavior on the Internet, but little is known about the variables that may predict patients' willingness to undergo online treatment and willingness to pay additionally for online treatment. Objective: This study analyzed sociodemographic variables, psychosocial variables, and variables of Internet usage to predict willingness to undergo online treatment and willingness to pay additionally for online treatment offered by the general practitioner (GP). Methods: An online survey of 1006 randomly selected German patients was conducted. The sample was drawn from an e-panel maintained by GfK HealthCare. Missing values were imputed; 958 usable questionnaires were analyzed. Variables with multi-item measurement were factor analyzed. Willingness to undergo online treatment and willingness to pay additionally for online treatment offered by the GP were predicted using 2 multiple regression models. Results: Exploratory factor analyses revealed that the disposition of patients' personality to engage in information-searching behavior on the Internet was unidimensional. Exploratory factor analysis with the variables measuring the motives for Internet usage led to 2 separate factors: perceived usefulness (PU) of the Internet for health-related information searching and social motives for information searching on the Internet. Sociodemographic variables did not serve as significant predictors for willingness to undergo online treatment offered by the GP, whereas PU (B=.092, P=.08), willingness to communicate with the GP more often in the future (B=.495, P<.001), health-related information--seeking personality (B=.369, P<.001), actual use of online communication with the GP (B=.198, P<.001), and social motive (B=.178, P=.002) were significant predictors. Age, gender, satisfaction with the GP, social motive, and trust in the GP had no significant impact on the willingness to pay additionally for online treatment, but it was predicted by health-related information--seeking personality (B=.127, P=.07), PU (B=--.098, P=.09), willingness to undergo online treatment (B=.391, P<.001), actual use of online communication with the GP (B=.192, P=.001), highest education level (B=.178, P<.001), monthly household net income (B=.115, P=.01), and willingness to communicate with the GP online more often in the future (B=.076, P=.03). Conclusions: Age, gender, and trust in the GP were not significant predictors for either willingness to undergo online treatment or to pay additionally for online treatment. Willingness to undergo online treatment was partly determined by the actual use of online communication with the GP, willingness to communicate online with the GP, health information--seeking personality, and social motivation for such behavior. Willingness to pay extra for online treatment was influenced by the monthly household net income category and education level. The results of this study are useful for online health care providers and physicians who are considering offering online treatments as a viable number of patients would appreciate the possibility of undergoing an online treatment offered by their GP. ",
doi="10.2196/jmir.5244",
url="http://www.jmir.org/2016/2/e32/",
url="http://www.ncbi.nlm.nih.gov/pubmed/26846162"
}


@Article{info:doi/10.2196/resprot.4519,
author="van Limburg, Maarten
and Wentzel, Jobke
and Sanderman, Robbert
and van Gemert-Pijnen, Lisette",
title="Business Modeling to Implement an eHealth Portal for Infection Control: A Reflection on Co-Creation With Stakeholders",
journal="JMIR Res Protoc",
year="2015",
month="Aug",
day="13",
volume="4",
number="3",
pages="e104",
keywords="business modeling",
keywords="co-creation",
keywords="eHealth",
keywords="guideline",
keywords="implementation",
keywords="road map",
keywords="stakeholder",
keywords="value",
abstract="Background: It is acknowledged that the success and uptake of eHealth improve with the involvement of users and stakeholders to make technology reflect their needs. Involving stakeholders in implementation research is thus a crucial element in developing eHealth technology. Business modeling is an approach to guide implementation research for eHealth. Stakeholders are involved in business modeling by identifying relevant stakeholders, conducting value co-creation dialogs, and co-creating a business model. Because implementation activities are often underestimated as a crucial step while developing eHealth, comprehensive and applicable approaches geared toward business modeling in eHealth are scarce. Objective: This paper demonstrates the potential of several stakeholder-oriented analysis methods and their practical application was demonstrated using Infectionmanager as an example case. In this paper, we aim to demonstrate how business modeling, with the focus on stakeholder involvement, is used to co-create an eHealth implementation. Methods: We divided business modeling in 4 main research steps. As part of stakeholder identification, we performed literature scans, expert recommendations, and snowball sampling (Step 1). For stakeholder analyzes, we performed ``basic stakeholder analysis,'' stakeholder salience, and ranking/analytic hierarchy process (Step 2). For value co-creation dialogs, we performed a process analysis and stakeholder interviews based on the business model canvas (Step 3). Finally, for business model generation, we combined all findings into the business model canvas (Step 4). Results: Based on the applied methods, we synthesized a step-by-step guide for business modeling with stakeholder-oriented analysis methods that we consider suitable for implementing eHealth. Conclusions: The step-by-step guide for business modeling with stakeholder involvement enables eHealth researchers to apply a systematic and multidisciplinary, co-creative approach for implementing eHealth. Business modeling becomes an active part in the entire development process of eHealth and starts an early focus on implementation, in which stakeholders help to co-create the basis necessary for a satisfying success and uptake of the eHealth technology. ",
doi="10.2196/resprot.4519",
url="http://www.researchprotocols.org/2015/3/e104/",
url="http://www.ncbi.nlm.nih.gov/pubmed/26272510"
}


@Article{info:doi/10.2196/resprot.3501,
author="van Meeuwen, PD Dorine
and van Walt Meijer, J. Quirine
and Simonse, WL Lianne",
title="Care Models of eHealth Services: A Case Study on the Design of a Business Model for an Online Precare Service",
journal="JMIR Res Protoc",
year="2015",
month="Mar",
day="24",
volume="4",
number="1",
pages="e32",
keywords="eHealth",
keywords="business model innovation",
keywords="strategic design",
keywords="precare",
keywords="service design",
keywords="visual modeling method",
keywords="care model",
abstract="Background: With a growing population of health care clients in the future, the organization of high-quality and cost-effective service providing becomes an increasing challenge. New online eHealth services are proposed as innovative options for the future. Yet, a major barrier to these services appears to be the lack of new business model designs. Although design efforts generally result in visual models, no such artifacts have been found in the literature on business model design. This paper investigates business model design in eHealth service practices from a design perspective. It adopts a research by design approach and seeks to unravel what characteristics of business models determine an online service and what are important value exchanges between health professionals and clients. Objective: The objective of the study was to analyze the construction of care models in-depth, framing the essential elements of a business model, and design a new care model that structures these elements for the particular context of an online pre-care service in practice. Methods: This research employs a qualitative method of an in-depth case study in which different perspectives on constructing a care model are investigated. Data are collected by using the visual business modeling toolkit, designed to cocreate and visualize the business model. The cocreated models are transcribed and analyzed per actor perspective, transactions, and value attributes. Results: We revealed eight new actors in the business model for providing the service. Essential actors are: the intermediary network coordinator connecting companies, the service dedicated information technology specialists, and the service dedicated health specialist. In the transactions for every service providing we found a certain type of contract, such as a license contract and service contracts for precare services and software products. In addition to the efficiency, quality, and convenience, important value attributes appeared to be: timelines, privacy and credibility, availability, pleasantness, and social interaction. Based on the in-depth insights from the actor perspectives, the business model for online precare services is modeled with a visual design. A new care model of the online precare service is designed and compiled of building blocks for the business model. Conclusions: For the construction of a care model, actors, transactions, and value attributes are essential elements. The design of a care model structures these elements in a visual way. Guided by the business modeling toolkit, the care model design artifact is visualized in the context of an online precare service. Important building blocks include:  provision of an online flow of information with regular interactions to the client stimulates self-management of personal health and service-dedicated health expert ensure an increase of the perceived quality of the eHealth service. ",
doi="10.2196/resprot.3501",
url="http://www.researchprotocols.org/2015/1/e32/",
url="http://www.ncbi.nlm.nih.gov/pubmed/25831094"
}


@Article{info:doi/10.2196/jmir.3390,
author="Miron-Shatz, Talya
and Shatz, Itamar
and Becker, Stefan
and Patel, Jigar
and Eysenbach, Gunther",
title="Promoting Business and Entrepreneurial Awareness in Health Care Professionals: Lessons From Venture Capital Panels at Medicine 2.0 Conferences",
journal="J Med Internet Res",
year="2014",
month="Aug",
day="06",
volume="16",
number="8",
pages="e184",
keywords="start-up",
keywords="entrepreneurship",
keywords="health technology",
keywords="capital funding, telehealth, eHealth, mobile health, health technology, technology transfer",
keywords="health 2.0",
keywords="business pitch",
keywords="entrepreneurship programs",
doi="10.2196/jmir.3390",
url="http://www.jmir.org/2014/8/e184/",
url="http://www.ncbi.nlm.nih.gov/pubmed/25100579"
}


@Article{info:doi/10.2196/jmir.3195,
author="Vermeulen, Joan
and Verwey, Ren{\'e}e
and Hochstenbach, MJ Laura
and van der Weegen, Sanne
and Man, Ping Yan
and de Witte, P. Luc",
title="Experiences of Multidisciplinary Development Team Members During User-Centered Design of Telecare Products and Services: A Qualitative Study",
journal="J Med Internet Res",
year="2014",
month="May",
day="19",
volume="16",
number="5",
pages="e124",
keywords="user-centered design",
keywords="telecare",
keywords="eHealth",
keywords="participation",
keywords="multidisciplinary team",
keywords="barriers and facilitators",
abstract="Background: User-centered design (UCD) methodologies can help take the needs and requirements of potential end-users into account during the development of innovative telecare products and services. Understanding how members of multidisciplinary development teams experience the UCD process might help to gain insight into factors that members with different backgrounds consider critical during the development of telecare products and services. Objective: The primary objective of this study was to explore how members of multidisciplinary development teams experienced the UCD process of telecare products and services. The secondary objective was to identify differences and similarities in the barriers and facilitators they experienced. Methods: Twenty-five members of multidisciplinary development teams of four Research and Development (R\&D) projects participated in this study. The R\&D projects aimed to develop telecare products and services that can support self-management in elderly people or patients with chronic conditions. Seven participants were representatives of end-users (elderly persons or patients with chronic conditions), three were professional end-users (geriatrician and nurses), five were engineers, four were managers (of R\&D companies or engineering teams), and six were researchers. All participants were interviewed by a researcher who was not part of their own development team. The following topics were discussed during the interviews: (1) aim of the project, (2) role of the participant, (3) experiences during the development process, (4) points of improvement, and (5) what the project meant to the participant. Results: Experiences of participants related to the following themes: (1) creating a development team, (2) expectations regarding responsibilities and roles, (3) translating user requirements into technical requirements, (4) technical challenges, (5) evaluation of developed products and services, and (6) valorization. Multidisciplinary team members from different backgrounds often reported similar experienced barriers (eg, different members of the development team speak a ``different language'') and facilitators (eg, team members should voice expectations at the start of the project to prevent miscommunication at a later stage). However, some experienced barriers and facilitators were reported only by certain groups of participants. For example, only managers reported the experience that having different ideas about what a good business case is within one development team was a barrier, whereas only end-users emphasized the facilitating role of project management in end-user participation and the importance of continuous feedback from researchers on input of end-users. Conclusions: Many similarities seem to exist between the experienced barriers and facilitators of members of multidisciplinary development teams during UCD of telecare products and services. However, differences in experiences between team members from various backgrounds exist as well. Insights into these similarities and differences can improve understanding between team members from different backgrounds, which can optimize collaboration during the development of telecare products and services. ",
doi="10.2196/jmir.3195",
url="http://www.jmir.org/2014/5/e124/",
url="http://www.ncbi.nlm.nih.gov/pubmed/24840245"
}


@Article{info:doi/10.2196/resprot.2547,
author="Van Velsen, Lex
and Wentzel, Jobke
and Van Gemert-Pijnen, EWC Julia",
title="Designing eHealth that Matters via a Multidisciplinary Requirements Development Approach",
journal="JMIR Res Protoc",
year="2013",
month="Jun",
day="24",
volume="2",
number="1",
pages="e21",
keywords="health care information systems",
keywords="health informatics",
keywords="requirements analysis",
keywords="software design techniques",
keywords="user-centered design",
abstract="Background: Requirements development is a crucial part of eHealth design. It entails all the activities devoted to requirements identification, the communication of requirements to other developers, and their evaluation. Currently, a requirements development approach geared towards the specifics of the eHealth domain is lacking. This is likely to result in a mismatch between the developed technology and end user characteristics, physical surroundings, and the organizational context of use. It also makes it hard to judge the quality of eHealth design, since it makes it difficult to gear evaluations of eHealth to the main goals it is supposed to serve. Objective: In order to facilitate the creation of eHealth that matters, we present a practical, multidisciplinary requirements development approach which is embedded in a holistic design approach for eHealth (the Center for eHealth Research roadmap) that incorporates both human-centered design and business modeling. Methods: Our requirements development approach consists of five phases. In the first, preparatory, phase the project team is composed and the overall goal(s) of the eHealth intervention are decided upon. Second, primary end users and other stakeholders are identified by means of audience segmentation techniques and our stakeholder identification method. Third, the designated context of use is mapped and end users are profiled by means of requirements elicitation methods (eg, interviews, focus groups, or observations). Fourth, stakeholder values and eHealth intervention requirements are distilled from data transcripts, which leads to phase five, in which requirements are communicated to other developers using a requirements notation template we developed specifically for the context of eHealth technologies. Results: The end result of our requirements development approach for eHealth interventions is a design document which includes functional and non-functional requirements, a list of stakeholder values, and end user profiles in the form of personas (fictitious end users, representative of a primary end user group). Conclusions: The requirements development approach presented in this article enables eHealth developers to apply a systematic and multi-disciplinary approach towards the creation of requirements. The cooperation between health, engineering, and social sciences creates a situation in which a mismatch between design, end users, and the organizational context can be avoided. Furthermore, we suggest to evaluate eHealth on a feature-specific level in order to learn exactly why such a technology does or does not live up to its expectations. ",
doi="10.2196/resprot.2547",
url="http://www.researchprotocols.org/2013/1/e21/",
url="http://www.ncbi.nlm.nih.gov/pubmed/23796508"
}


@Article{info:doi/10.2196/jmir.1674,
author="van Limburg, Maarten
and van Gemert-Pijnen, EWC Julia
and Nijland, Nicol
and Ossebaard, C. Hans
and Hendrix, MG Ron
and Seydel, R. Erwin",
title="Why Business Modeling is Crucial in the Development of eHealth Technologies",
journal="J Med Internet Res",
year="2011",
month="Dec",
day="28",
volume="13",
number="4",
pages="e124",
keywords="Business model",
keywords="cocreation",
keywords="collaboration",
keywords="eHealth",
keywords="implementation",
keywords="multidisciplinary",
keywords="stakeholder",
keywords="sustainability",
keywords="value creation",
abstract="The impact and uptake of information and communication technologies that support health care are rather low. Current frameworks for eHealth development suffer from a lack of fitting infrastructures, inability to find funding, complications with scalability, and uncertainties regarding effectiveness and sustainability. These issues can be addressed by defining a better implementation strategy early in the development of eHealth technologies. A business model, and thus business modeling, help to determine such an implementation strategy by involving all important stakeholders in a value-driven dialogue on what the technology should accomplish. This idea also seems promising to eHealth, as it can contribute to the whole development of eHealth technology. We therefore suggest that business modeling can be used as an effective approach to supporting holistic development of eHealth technologies. The contribution of business modeling is elaborated in this paper through a literature review that covers the latest business model research, concepts from the latest eHealth and persuasive technology research, evaluation and insights from our prior eHealth research, as well as the review conducted in the first paper of this series. Business modeling focuses on generating a collaborative effort of value cocreation in which all stakeholders reflect on the value needs of the others. The resulting business model acts as the basis for implementation. The development of eHealth technology should focus more on the context by emphasizing what this technology should contribute in practice to the needs of all involved stakeholders. Incorporating the idea of business modeling helps to cocreate and formulate a set of critical success factors that will influence the sustainability and effectiveness of eHealth technology. ",
doi="10.2196/jmir.1674",
url="http://www.jmir.org/2011/4/e124/",
url="http://www.ncbi.nlm.nih.gov/pubmed/22204896"
}