Health information technology to improve care for people with multiple chronic conditions

Abstract

Objective To review evidence regarding the use of Health Information Technology (health IT) interventions aimed at improving care for people living with multiple chronic conditions (PLWMCC) in order to identify critical knowledge gaps. Data Sources We searched MEDLINE, CINAHL, PsycINFO, EMBASE, Compendex, and IEEE Xplore databases for studies published in English between 2010 and 2020. Study Design We identified studies of health IT interventions for PLWMCC across three domains as follows: self‐management support, care coordination, and algorithms to support clinical decision making. Data Collection/Extraction Methods Structured search queries were created and validated. Abstracts were reviewed iteratively to refine inclusion and exclusion criteria. The search was supplemented by manually searching the bibliographic sections of the included studies. The search included a forward citation search of studies nested within a clinical trial to identify the clinical trial protocol and published clinical trial results. Data were extracted independently by two reviewers. Principal Findings The search yielded 1907 articles; 44 were included. Nine randomized controlled trials (RCTs) and 35 other studies including quasi‐experimental, usability, feasibility, qualitative studies, or development/validation studies of analytic models were included. Five RCTs had positive results, and the remaining four RCTs showed that the interventions had no effect. The studies address individual patient engagement and assess patient‐centered outcomes such as quality of life. Few RCTs assess outcomes such as disability and none assess mortality. Conclusions Despite a growing body of literature on health IT interventions or multicomponent interventions including a health IT component for chronic disease management, current evidence for applying health IT solutions to improve care for PLWMCC is limited. The body of literature included in this review provides critical information on the state of the science as well as the many gaps that need to be filled for digital health to fulfill its promise in supporting care delivery that meets the needs of PLWMCC.

Collecting and sharing self-generated health and lifestyle data: Understanding barriers for people living with long-term health conditions – a survey study

“…A recent literature review suggested that a lack of attention has been given to understanding attitudes towards the sharing of health and lifestyle data with third parties, which suggests the need for future study. 70 …”

Cultivating informatics capacity for multimorbidity: A learning health systems use case

“…Learning health systems are data-driven, healthcare delivery processes of continuous quality improvement, providing patients with higher quality, safer, and more effective care by utilizing informatics and data science to translate research into evidence-based practice. 31,32 However, opportunities to improve multimorbidity patient outcomes are widely constrained by the current informatics infrastructure that is used to characterize the outcomes of patients with multimorbidity. 2,4,6,[31][32][33][34][35] To date, these informatics constraints have included the subjectivity (i.e., recall bias) of analyzing self-reported data gathered through national databases (i.e., National Health Interview Survey) and the focus on specific sets of chronic conditions versus all combinations that could potentially encompass multimorbidity (i.e., Behavioral Risk Factor Surveillance System Surveys).…”

Prediction of Future Health Care Utilization Through Note-extracted Psychosocial Factors

“…Thus, adding psychosocial variables for patients with higher multimorbidity appeared to be a more consistently powerful predictor of utilization than attempting to use individual conditions; this lends itself to use in more comprehensive models of care management and coordination. 44,45 Previous work has looked at the benefits of collecting SDOH as psychosocial factors. A review showed increasing rates of a structured collection of social determinants in EHRs showed promise in use in prediction and quality improvement, but many barriers exist, including a lack of agreement on how, when, and why to collect the data.…”

“…For instance, patients with diabetes had better prediction models for hospitalizations from housing insecurity and financial insecurity, but not for depression and social isolation. Thus, adding psychosocial variables for patients with higher multimorbidity appeared to be a more consistently powerful predictor of utilization than attempting to use individual conditions; this lends itself to use in more comprehensive models of care management and coordination 44,45…”