Longitudinal clinical decision support for assessing decisions over time: State-of-the-art and future directions

Tyler J Loftus; Jeremy A Balch; Jenna L Marquard; Jessica M Ray; Brian S Alper; Neeraj Ojha; Azra Bihorac; Genevieve Melton-Meaux; Gopal Khanna; Christopher J Tignanelli

doi:10.1177/20552076241249925

Longitudinal clinical decision support for assessing decisions over time: State-of-the-art and future directions

Digit Health. 2024 May 2:10:20552076241249925. doi: 10.1177/20552076241249925. eCollection 2024 Jan-Dec.

Authors

Tyler J Loftus^{1

2}, Jeremy A Balch^{1

2}, Jenna L Marquard^{3

4}, Jessica M Ray⁵, Brian S Alper^{6

7}, Neeraj Ojha⁸, Azra Bihorac^{2

9}, Genevieve Melton-Meaux^{4

10

11}, Gopal Khanna¹², Christopher J Tignanelli^{4

10

13}

Affiliations

¹ Department of Surgery, University of Florida Health, Gainesville, FL, USA.
² Intelligent Critical Care Center (IC3), University of Florida Health, Gainesville, FL, USA.
³ School of Nursing, University of Minnesota, Minneapolis, MN, USA.
⁴ Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA.
⁵ Department of Health Outcomes and Biomedical Informatics, University of Florida Health, Gainesville, FL, USA.
⁶ Computable Publishing LLC, Ipswich, MA, USA.
⁷ Scientific Knowledge Accelerator Foundation, Ipswich, MA, USA.
⁸ EunoChains LLC, Potomac, MD, USA.
⁹ Department of Medicine, University of Florida Health, Gainesville, FL, USA.
¹⁰ Department of Surgery, University of Minnesota, Minneapolis, MN, USA.
¹¹ Center for Learning Health Systems Science, University of Minnesota, Minneapolis, MN, USA.
¹² Medical Industry Leadership Institute, Carlson School of Management, University of Minnesota, Minneapolis, MN, USA.
¹³ Program for Clinical Artificial Intelligence, Center for Learning Health Systems Science, University of Minnesota, Minneapolis, MN, USA.

Abstract

Objective: Patients and clinicians rarely experience healthcare decisions as snapshots in time, but clinical decision support (CDS) systems often represent decisions as snapshots. This scoping review systematically maps challenges and facilitators to longitudinal CDS that are applied at two or more timepoints for the same decision made by the same patient or clinician.

Methods: We searched Embase, PubMed, and Medline databases for articles describing development, validation, or implementation of patient- or clinician-facing longitudinal CDS. Validated quality assessment tools were used for article selection. Challenges and facilitators to longitudinal CDS are reported according to PRISMA-ScR guidelines.

Results: Eight articles met inclusion criteria; each article described a unique CDS. None used entirely automated data entry, none used living guidelines for updating the evidence base or knowledge engine as new evidence emerged during the longitudinal study, and one included formal readiness for change assessments. Seven of eight CDS were implemented and evaluated prospectively. Challenges were primarily related to suboptimal study design (with unique challenges for each study) or user interface. Facilitators included use of randomized trial designs for prospective enrollment, increased CDS uptake during longitudinal exposure, and machine-learning applications that are tailored to the CDS use case.

Conclusions: Despite the intuitive advantages of representing healthcare decisions longitudinally, peer-reviewed literature on longitudinal CDS is sparse. Existing reports suggest opportunities to incorporate longitudinal CDS frameworks, automated data entry, living guidelines, and user readiness assessments. Generating best practice guidelines for longitudinal CDS would require a greater depth and breadth of published work and expert opinion.

Keywords: CDS; CDSS; Shared decision-making; evolve; healthcare.

Publication types

Review