Evidence map›Paper›PMID 38708184›Full record

ReviewDigital health

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

Abstract readReview
In one paragraph

Review in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Tyler J LoftusDepartment of Surgery, University of Florida Health, Gainesville, FL, USA.ORCID https://orcid.org/0000-0001-5354-443X
Jeremy A BalchDepartment of Surgery, University of Florida Health, Gainesville, FL, USA.ORCID https://orcid.org/0000-0002-1826-7884
Jenna L MarquardSchool of Nursing, University of Minnesota, Minneapolis, MN, USA.
Jessica M RayDepartment of Health Outcomes and Biomedical Informatics, University of Florida Health, Gainesville, FL, USA.
Brian S AlperComputable Publishing LLC, Ipswich, MA, USA.
Neeraj OjhaEunoChains LLC, Potomac, MD, USA.
Azra BihoracIntelligent Critical Care Center (IC3), University of Florida Health, Gainesville, FL, USA.
Genevieve Melton-MeauxInstitute for Health Informatics, University of Minnesota, Minneapolis, MN, USA.
Gopal KhannaMedical Industry Leadership Institute, Carlson School of Management, University of Minnesota, Minneapolis, MN, USA.
Christopher J TignanelliInstitute for Health Informatics, University of Minnesota, Minneapolis, MN, USA.ORCID https://orcid.org/0000-0002-8079-5565

Funding

Aligning Patient Acuity with Intensity of Care after SurgeryK23GM140268 · NIGMS · UNIVERSITY OF FLORIDA · PI LOFTUS, TYLER J · 2020 to 2023
$619k
NIGMS NIH HHS K23 GM140268
6 · The paper itself

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.

Indexed as

CDSCDSSevolvehealthcareShared decision-making

Identifiers

PMID38708184
PMCPMC11067677

What OpenQuestion holds

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LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.