The Integration of Clinical Decision Support Systems Into Telemedicine for Patients With Multimorbidity in Primary Care Settings: Scoping Review

Introduction

Multimorbidity, the presence of more than one condition in a single individual [1], is a global phenomenon that has impacted individual health and imposed national economic burdens [2-5]. People with multiple illnesses tend to have a lower quality of life, higher disability, and higher incidence of coexisting acute diseases compared to those without multiple illnesses [6,7]. It has been found that multimorbidity increases health and hospital expenditures through increased costs for medication, outpatient care, and hospitalization [2-5,8]. The raised workload on the medical staff, especially in a primary care setting, results in a lower quality of care. This results in patients with multimorbidity becoming less likely to receive patient-centered care and appropriate continuity of care [9,10].

Providing effective care for people with multimorbidity is complex, as patients and health providers face many challenges [11-15]. From the patient’s perspective, one of the barriers to reaching effective health outcomes is the complexity of the care process, which includes multiple appointments, refilling multiple prescriptions, and difficulty in understanding complex medical details. Patients are burdened by this process, particularly when all disease guidelines are followed [14]. Another barrier is the difficulty of access to care. As medical resources are scarce, the provision of health care facilities might be insufficient, leading to inadequate service centers, consultation restrictions, and limited geographic access [11-13]. From a provider’s perspective, multimorbidity contributes to more complex disease management and increased workload [15]. Patients with multiple diseases are also usually affected by polypharmacy, requiring more awareness of drug interactions and adverse events [16,17].

Information and communication technology (ICT) has been introduced to help deal with the complexities and challenges of caring for patients with multimorbidity [18]. Common ICT in health care services includes telemedicine and clinical decision support systems (CDSSs). Telemedicine, or telehealth, refers to the use of technology to improve patient outcomes by delivering health service at a distance. It comprises several basic medical care functions, including monitoring, consulting, mentoring, and providing medical knowledge [19]. Telemedicine has been claimed as an essential tool for improving health service access (increasing convenience of health care visits and removing geographic barriers) and reducing patient burdens from complex care processes (decreasing the frequency of appointments, providing necessary medical knowledge, and enhancing patient self-management) [19-23]. CDSSs are another common ICT used in health care. They have been developed in an attempt to assist health providers in making medical decisions by reducing the complexity of disease management. Computer-based programming has been used to analyze patient data from electronic health records (EHRs) and integrate the analyzed results with clinical knowledge and guidelines. The program then offers suitable choices for patients and providers through prompts, reminders, or treatment recommendations at the appropriate times [24]. These decision-support tools can be helpful for providers when managing patients with multiple conditions, as they reduce the burden and complexity of analyzing multiple problems and integrating different clinical guidelines. Assisting providers in complex decision-making allows patients to obtain better clinical outcomes and promotes both patient and provider satisfaction [25].

Both telemedicine and CDSSs could be potential tools for strengthening the capability and capacity of health care services in caring for multimorbidity in primary care settings where medical resources are limited [18]. However, each element of telemedicine and CDSSs is often examined separately as a tool for caring for multimorbidity, and there is great heterogeneity in implementation. Telemedicine has been used for simple patient education as well as more complex consultations and case management. CDSSs have variability in data inputs, intended users, and actions (ie, outputs). Thus, there are several gaps in knowledge about how to integrate CDSSs into telemedicine. Moreover, it is uncertain to what extent these integrated technological interventions can help improve outcomes for patients with multimorbidity. Therefore, the aims of this review are to (1) provide a broad review of how CDSSs have been integrated into each function of telemedicine for multimorbid patients in primary care, (2) summarize the effectiveness of telemedicine and CDSS interventions by looking at clinical and patient outcomes, and (3) identify gaps in the literature.

Methods

This scoping review used the PRISMA (Preferred Reporting Items for Scoping Reviews) checklist. The review protocol was registered on PROSPERO (CRD42021293444). Scoping reviews help identify how research is conducted in a given area, identify key characteristics related to particular concepts, and identify and analyze research gaps [26].

Search Strategy

An online literature search was conducted in September 2021 and updated in November 2021. The electronic databases included PubMed, Embase, CINAHL, and Cochrane. Only English articles published from 2015 to 2021 were included. We limited the publications to those published after 2015 due to the fact that CDSSs only started to be commonly used in health care, health science, and clinical informatics in 2015 [27]. Online databases showed a significant increase in the number of publications related to the use of CDSSs in telemedicine in 2015 and an exponential increase since the COVID-19 pandemic [28]. Additional checking of the reference lists of relevant papers was also done. The initial search terms were based on 2 concepts: clinical decision support system AND multimorbidity (eg, multiple conditions, multidrug therapy, concurrent illness, polypharmacy). The term telemedicine was considered in the articles that had both search terms. The full search terms are shown in Multimedia Appendix 1. An additional reference search was performed from articles included in the review. An extended search was then performed by adding more search terms for several specific conditions commonly examined in multimorbidity found in the initial searches. These conditions included diabetes, cardiovascular disease, hypertension, dyslipidemia, and heart failure. Additional search terms are shown in Multimedia Appendix 2. In this review, aside from having 2 or more conditions in a single individual, the word polypharmacy (ie, the act of taking ≥5 medicines) was inferred as implying multimorbidity, since prescribing 5 or more drugs for only 1 disease is rare [29].

Eligibility Criteria

The inclusion criteria were (1) the study focused on patients with multiple chronic conditions; (2) the intervention included any services using telemedicine and a CDSS; and (3) the setting was restricted to primary care.

We excluded (1) studies investigating telemedicine alone or a CDSS alone; (2) studies including patients with a single disease or multiple diseases that were the complication or consequence of each other; (3) studies without reported results, such as protocols, editorials, comments, perspectives, and correspondence; and (4) review articles.

Study Selection

Once duplicate results were removed, 4 authors (NW, SP, NB, and KP) were responsible for the screening and data extraction process. Abstract and full-paper screening was performed by at least 2 authors independently for each paper. Any disagreement on eligibility was identified and resolved by the senior author (C Angkurawaranon) if necessary.

Data Extraction

Similar to the screening process, 4 authors (NW, SP, NB, and KP) were responsible for data extraction. For each study selected, data were extracted by 2 authors working separately, and any disagreement was discussed with the senior author. The data included the author’s name, year of publication, year the study was conducted, country, study design, population characteristics, mean age of the population, sample size, details of the intervention, and outcomes of the studies in all methods of measurement (eg, mean difference, risk ratio, and odds ratio). Data from the same trial but with results published as multiple papers are summarized as 1 trial. The flow chart of the selection process is illustrated in Figure 1.

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Data Items

Telemedicine and CDSSs

We extracted data on telemedicine components and the structure of the CDSSs by adopting the definition of telemedicine components of Lee et al [19] and the structure of CDSSs from Fraccaro et al [30]. The structure of the CDSSs was extracted into 6 domains: software and hardware, source of input, input data, task, output (ie, content), and users and decision makers. The tasks of the CDSSs were categorized into 6 main purposes: disease prevention; disease diagnosis; patient management according to guidelines (or pathways); medication-related issues, such as drug interactions; patient education; and patient self-management.

For telemedicine, we used a classification modified from Lee et al [19]. We classified telemedicine into four categories: (1) tele-mentoring or teleconsultation, (2) tele–case management, (3) telemonitoring, and (4) tele-education. Tele-mentoring, which includes teleconsultation, is defined as the provision of medical guidance, disease-specific suggestions, and specific answers to patients by health providers. Tele–case management is defined as any communication between primary care providers and other specialists, case transferal, case referral, or medicine adjustment. The definitions of telemonitoring and tele-education followed the original taxonomy by Lee et al [19], defined as the process of distant transmission of patients’ health status and the use of technologies for the delivery of distance learning, respectively.

Effectiveness of Telemedicine and CDSSs

The effectiveness of the interventions was initially determined by looking at clinical outcomes or indicators of disease control, including biomarkers, such as hemoglobin A_1C (HbA_1c), fasting blood sugar level, blood pressure, body weight, and BMI.

Other outcomes of interest included medication management, patient self-management, and disease management. Medication management included documented adverse drug events (ADEs), medication errors, and medication discontinuation. Patient self-management included compliance with self-monitoring (such as home monitoring of blood pressure and capillary blood sugar level) and change in health behaviors. Finally, disease management was assessed by the ability of the system to provide diagnoses and further steps according to practice guidelines at the appropriate time. Other outcomes beyond this scope but related to the objectives were also included if reported.

Risk of Bias Assessment

As no observational studies were included, the Cochrane Risk of Bias Tool (version 2) was used to assess the risk of bias among experimental studies as low, high, or unclear [31]. The biases assessed had five aspects: (1) bias arising from the randomization process, (2) bias due to deviations from intended interventions, (3) bias due to missing outcome data, (4) bias in the measurement of the outcome, and (5) bias in the selection of the reported results. This step was performed by 2 authors independently, and any disagreement between them was resolved by discussion with the senior authors.

Synthesis of Results

For each study, the extracted data were qualitatively summarized. Key characteristics across studies were summarized using counts and percentages. The extracted data showed heterogeneity in study populations, designs, interventions, and outcomes. Therefore, meta-analysis of outcomes was not conducted.

Results

Search Selection

There were 829 potentially relevant publications from 4 online databases. Abstract screening for eligibility was done by 2 authors independently, and 25 full-text articles were assessed. Finally, 7 articles were included and extracted in this review. The flow chart of the selection process is illustrated in Figure 1.

Characteristics of Included Studies

All articles included in this review were experimental studies published between 2017 and 2020: 3 RCTs [32-34] and 4 non-RCTs [35-38]. They were mostly conducted in high-income countries such as the United States [32,33], England [38], Canada, Italy, and Spain [37]. Participants were patients with ≥2 chronic conditions aged from 33 years to older than 60 years. The existing chronic conditions included in this review were diabetes mellitus [32,34,35,38], hypertension [31,33,34,36,37], atrial fibrillation [37,38], and polypharmacy [33,36], with only 1 study focusing on diabetes mellitus in pregnancy [37]. The details of each study by year and chronic conditions are shown in Table 1.

Risk of Bias in Studies

In general, most studies were considered as having a low risk of bias for missing outcome data, measurement bias, and reporting bias. However, a high risk of bias due to a lack of randomization was found in several articles [35-37], and unclear evidence of performing an intention-to-treat analysis was found in 5 studies [33-38]. The results of the risk of bias assessment are illustrated in Table 2.

System Design of CDSSs in Telemedicine

Overview

Among the 7 included papers, telemedicine interventions were mainly used for monitoring, consulting, and case management (Multimedia Appendix 3 [32-38]). Only 1 study mentioned tele-education. This review found that the functions of telemedicine in treatment for multimorbidity often overlapped and that there often was more than 1 function in a single intervention, especially for tele–case management, which was not used alone without combination with other functions, such as telemonitoring. The tasks of CDSSs in telemedicine can be summarized into 6 categories. They were predominantly used for patient management according to guidelines, medication-related issues, disease diagnosis, patient education, and patient self-management, but not for disease prevention (Multimedia Appendix 4 [32-38]). This review found that CDSSs can complete several tasks, that is, they can manage multiple responsibilities at once, as demonstrated by their ability to provide notifications to patients and recommendations to providers at the same time. The following sections provide an overview of the system designs of CDSSs according to their main functions in telemedicine.

CDSS Designs for Use in Telemonitoring

Three papers reported interventions that used telemedicine for remote monitoring of patients with polypharmacy, atrial fibrillation, and gestational diabetes mellitus, as well as patients who were taking medicine to treat hypertension, diabetes, insomnia, and depression [32,33,37]. Sources of data input for the CDSSs varied, including manual assessment over the phone, wearable sensors, self-entering in a smartphone app, and EHRs. These sources gathered the necessary information from patients into the CDSS, which then detected ADEs, interpreted the data from sensors, and mapped patient data to clinical guidelines. The type of output depended on the task (Table 3). For example, CDSSs used for ADE detection and sensor interpretation may give feedback in the form of drug alerts and clinical issue notifications, respectively. In contrast, CDSSs used for guideline and health record interpretation may show outputs in the form of recommendations for patient management. Feedback was mostly sent to health providers, who were the main decision-makers in the patient care process. However, in one intervention, feedback was directly sent to patients [37], because they are the ones who decide their further management, such as visiting the outpatient department and modifying their behavior.

CDSS Designs for Use in Teleconsultation

Four studies investigated the use of telemedicine for consultation [34,35,37,38]. Health conditions examined included diabetes, hypertension, atrial fibrillation, and polypharmacy. CDSSs were used to confirm diagnoses based on patients’ information and diagnostic criteria, generate treatment plans based on the guidelines, and provide appropriate advisory materials for those with risky behaviors (Table 4). Some simple feedback created by the system was sent back to patients, including reminders for follow-up visits and medication adherence, general advice for patient self-care, and automated answers to patients’ common queries. However, for more complicated management and responses to more complex queries, health providers were offered treatment choices, and it was their task to answer patients’ questions using the information given by the CDSS.

CDSS Designs for Use in Tele–Case Management

Two articles evaluated the use of CDSSs in managing patients with diabetes, hypertension, and polypharmacy [35,36]. Input data was mostly entered by health care providers from the patient registry and admission notes, including information regarding chief concerns, current illness, past medical history, current medications, laboratory results, and imaging. The outputs were notifications, clinical management recommendations, and medication management (Table 5). However, feedback was used among primary care providers and patients and could also be used between primary care providers and other specialists. For example, in the study by McDonald et al [36], patients’ prescriptions were circulated between community health providers and tertiary health facilities. Moreover, ICT systems were also designed for multiple-user registration, which increases the accessibility of systems to serve multidisciplinary teams, including physicians, nurses, nurse technicians, and support teams.

CDSS Designs for Use in Tele-Education

Only one study investigated the use of CDSSs for tele-education [36]. This system was used for patients with polypharmacy to interpret the patients’ medication data according to deprescribing rules and screening for high-risk prescriptions (Table 5). The system instructed patients with medication-related educational content and provided suggestions on tapering to providers. Patients or caregivers spontaneously received learning materials related to their current medications; for example, they learned details on the harms of polypharmacy and on deprescription.

Effectiveness of the Interventions and Other Outcomes

Six different types of outcomes were assessed in the 7 studies, including disease control, medication management, patient self-management, patient care, doctor-patient communication, and feasibility and satisfaction of intervention (Tables 2-5 and Multimedia Appendix 5 [32-38]). For disease control, 1 study conducted with patients with hypertension or diabetes showed that biomedical parameters were not significantly different from baseline to after completion of the intervention [34]. These parameters included blood pressure, BMI, fasting blood glucose level, HbA_1c, and lipid profile. Three studies reported positive outcomes for medication management among the intervention groups, with more correction of medication discrepancies, fewer ADEs, and more appropriate medication discontinuation. However, the number of medications did not significantly decrease [32,33,36]. For patient self-management, 3 studies reported related outcomes [34,37,38]. Participants in the intervention group had higher compliance to biomarker monitoring (ie, electrocardiograms, blood glucose, blood pressure, and urine tests). Still, a significant change in risky behaviors was not seen (eg, tobacco and alcohol use). For disease management, the interventions improved the accuracy of diagnoses and shortened the time from diagnosis to initiation of treatment [37]. Clinicians who use CDSSs as smartphone apps were more likely to change their diagnoses and treatments to more appropriate ones; for example, they were more likely to start early insulin therapy for patients with diabetes.

Other associated outcomes assessed were communication between providers and patients and the program’s feasibility. For doctor-patient communication, the interventions enhanced patient participation, as can be seen by a higher patient involvement rate, more active patient communication, more facilitative clinician communication, and more medication-related communication [32]. Users from multidisciplinary teams that included physicians, nurses, and other health professionals demonstrated satisfaction with the intervention [35], but no satisfaction assessment was performed among patients.

Discussion

Principal Findings

This scoping review aims to explore how CDSSs can be integrated into telemedicine services for caring for people with multimorbidity. Our review found that CDSSs have been incorporated for telemonitoring, teleconsultation, tele–case management, and tele-education. However, the structure of CDSSs, such as data input, tasks, output, and intended users or decision-makers, varied. With limited studies, mostly from developed countries, and inconclusive evidence on the clinical effectiveness of the interventions, more research is needed to examine how CDSSs can be successfully integrated to improve patient outcomes in multimorbidity.

The evidence suggested that CDSSs can be integrated into telemedicine and used to improve the capacity of telehealth services. Telemonitoring is commonly used for multimorbidity, as it allows patients to update their health-related information (eg, blood tests and health behaviors) via online platforms and monitor patient health status (eg, blood pressure and electrocradiograms) in real-time by connecting with remote sensors or wearable devices. CDSSs play an important role in detecting abnormalities and giving alerts or feedback to patients and providers at an appropriate time. For multimorbidity, integration of CDSSs with telemonitoring could be helpful for patients who need close monitoring of their health status but do not have an indication for hospital admission, as they can be monitored at home and can keep a sense of contact with their providers [39]; this includes patients with cardiovascular disease [40], allergic diseases [41], and COVID-19 [42].

For consultation and case management, integrating CDSSs with strategies to shift tasks from more specialized to less specialized health providers could reduce the burden of management for multimorbidity and increase the impact of health care [43]. CDSSs can reduce the workload of medical staff by automating responses to common patient queries and increasing the potential of nonprofessional providers to manage patient problems. This is relevant for managing multimorbidity, as evidence-based and real-time data and algorithms can improve decision-making for community health workers and primary care providers, helping them to make more accurate decisions across the multitude of situations they encounter [44]. Online platforms allow them to consult with specialists when the situation needs expert opinion and management. However, when incorporating CDSSs into primary care and the community, it is important to develop a feasible and acceptable intervention for frontline health workers. Digital tool training and internet connectivity in the areas where the CDSS will be used must also be provided [45].

The review identified several gaps in evidence when examining the incorporation of CDSSs into telemedicine to manage multimorbidity. First, there was a small number of clinical trials and a lack of diversity in chronic conditions. This review summarized the interventions applied for people with hypertension, diabetes, polypharmacy, and atrial fibrillation, but these 4 conditions are likely inadequate to represent all major chronic diseases commonly found in patients with multimorbidity. A study conducted in Cambodia, Myanmar, Thailand, and Vietnam revealed that the 3 most common chronic conditions with comorbidities were hypertension (37.4%), depression (34.4%), and digestive diseases (32%) [46]. Other evidence shows that depression, diabetes, cancer, and cardiovascular disease are the most common starting conditions before the accumulation over time of comorbidities and conditions, such as osteoarthritis and dementia [47-49]. It should be kept in mind that mental illness and cancer, diseases often associated with multimorbidity that account for a large proportion of coexisting conditions [50-55], were not found in this review.

Second, there was limited evidence for using CDSSs for screening, whether for early prevention or early diagnosis. This might be due to the inclusion criteria of the studies, which allowed only patients who were already diagnosed with multiple diseases to enroll. However, the previous evidence shows that CDSSs could potentially be used to help identify health issues in people who are not currently experiencing symptoms (ie, early detection and screening), such as dementia [56], cardiovascular disease [57], breast cancer [58,59], and pancreatic cancer [60]. Studies investigating the role of CDSSs in detecting multiple chronic diseases are still in the area of active research and development.

Third, the role of CDSSs for patients with multimorbidity remained relatively underexamined. Most of the feedback (eg, prompts and alerts) was sent to providers rather than to patients. Patients are informed less, and providers are the main decision-makers who decide which further steps of treatment should be taken. Low patient engagement makes patient-centered services difficult and could lead to negative consequences, such as poorer health outcomes, decreased adherence to the treatment, and lack of trust in health care providers [61]. Therefore, the ICT system should provide more opportunities for patients to be involved, such as by regularly giving them feedback, informing them about their health status, and delegating some decision-making responsibility to them.

There is a lack of evidence on how telemedicine and CDSS interventions can be used in resource-limited areas. Articles in this review were mostly conducted in high-income countries. While it has been claimed that telemedicine can eliminate geographic barriers and improve access to care, several challenges have been identified as limiting patient access to interventions, including technology access, digital literacy, financial support, regulations on telemedicine use, socioeconomic status, and sociopolitical situations [62,63].

Moreover, the effectiveness of interventions in this review was examined mainly by measuring disease-specific biomarkers, patient behavioral change, and patient care processes in health care facilities. However, as previously mentioned, one major limitation in drawing inferences on effectiveness was the limited number of conditions being assessed. Furthermore, other measures of effectiveness, important for multimorbidity, have not been explored, such as patient-reported outcomes, care coordination, and cost-effectiveness. For multimorbidity, patient-reported outcome measures are an essential tool to assess patients’ perspectives on their health status and the impact of interventions. They provide information for achieving health system goals and evaluating patients’ quality of care [64]. Measuring care coordination, which includes establishing responsibility, communication, goal setting, care plan development, and monitoring, can potentially be useful [65]. And cost-effectiveness studies are likely needed to help analyze the price of inputs for one unit gained of health outcomes of the intervention. This analytic tool compares the cost of the intervention with the cost of alternatives to achieve the same goal [66,67]. This is highly relevant for ICT interventions where infrastructure and workforce to support delivery can still be limited in developing countries [68].

It should also be noted that the literature suggests that there are multiple barriers to adopting CDSSs and telemedicine [69]. Software development requires huge budgets and a large number of work hours; thus, the intervention’s cost and benefits need to be carefully estimated before investment, but as reflected in the review, data are limited for people with multimorbidity. Another barrier to implementation is acceptance by health care providers. On the one hand, CDSSs and telemedicine provide useful information to promote health, assist treatment, and improve patient care. On the other hand, it has been claimed by physicians that it causes significant delays in their daily routine. This finding was related to evidence that health care professionals will accept the intervention after considering how it affects patient disease management and workflows [70]. Some experts felt that this technology could not replace their skills and experience in disease diagnosis and management and that the ability of CDSSs to work across different care settings was still questionable [69]. Hence, making patients and health care workers satisfied with the technology calls for system designs that are both feasible and usable. A process evaluation of how to reduce time used for CDSSs and telemedicine in routine daily care workflows and how to make providers adopt the program is likely needed.

Strengths and Limitations

The novelty of this review is the attempt to summarize how CDSSs have been integrated into telemedicine services for patients with multimorbidity in primary care. However, there are some limitations. Telemedicine, CDSSs, and multimorbidity are broad concepts. Despite a comprehensive search strategy, some relevant literature may have been missed. A past review exploring the role of CDSSs and telemedicine in critical care [71] included only 2 studies, while a review examining the role of CDSSs and telemedicine in prehospital emergency care [72] included 7 studies. Our review was limited to 4 databases (PubMed, Embase, CINAHL, and Cochrane) and to literature published in English. Thus, non-English articles and articles that were published in other databases may have been missed. All the included studies were conducted in high-income countries with variations in design, sample size, study protocol, and outcomes. This makes it difficult to compare effectiveness across studies and make a clear summary or conclusion. Moreover, digital health is new in the medical research field, and the number of studies is still limited; ongoing research might have been missed.

Conclusion

There is a role for telemedicine and CDSSs in supporting patients with multimorbidity. CDSSs can likely be integrated into telehealth services to improve the quality and accessibility of care, resulting in a reduction in the burden of disease. However, various aspects of such interventions need to be explored, such as ways to expand the tasks included in CDSSs and the role of patient involvement, and interventions for a greater variety of conditions should be included. Additional assessments of the effectiveness of the intervention are required, including patient-reported outcomes, care coordination, and cost-effectiveness. Moreover, as the interventions have heterogeneous purposes and situational contexts, the feasibility and usability of the systems from the points of view of both health care providers and patients should be investigated in order to develop an ICT system that meets the needs of the local context.