Evidence map›Paper›PMID 39602213›Full record

ReviewInteractive journal of medical research2024

Benefits of Clinical Decision Support Systems for the Management of Noncommunicable Chronic Diseases: Targeted Literature Review.

Klaudia Grechuta, Pedram Shokouh, Ahmad Alhussein, Dirk Müller-Wieland, Juliane Meyerhoff, Jeremy Gilbert, Sneha Purushotham, Catherine Rolland

Abstract readReview
In one paragraph

Review in Interactive journal of medical research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 3 pooled it
–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

17 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Observational
  12. Review
  13. Article
  14. Article
  15. Article
  16. Article
  17. 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

8 authors.

Klaudia GrechutaBoehringer Ingelheim International GmbH, Ingelheim am Rhein, Germany.ORCID https://orcid.org/0000-0002-4954-1372
Pedram ShokouhAdivus Medical Consultancy Mpv., Aarhus, Denmark.ORCID https://orcid.org/0000-0002-4514-0045
Ahmad AlhusseinBoehringer Ingelheim International GmbH, Ingelheim am Rhein, Germany.ORCID https://orcid.org/0000-0003-1000-8308
Dirk Müller-WielandDepartment of Internal Medicine I, University Hospital Aachen, Aachen, Germany.ORCID https://orcid.org/0000-0002-8807-6442
Juliane MeyerhoffBoehringer Ingelheim International GmbH, Ingelheim am Rhein, Germany.ORCID https://orcid.org/0009-0009-3745-7753
Jeremy GilbertSunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada.ORCID https://orcid.org/0000-0001-7456-9396
Sneha PurushothamPPD Australia Pty Ltd, Sydney, Australia.ORCID https://orcid.org/0009-0006-5870-1661
Catherine RollandEvidera, The Ark, London, United Kingdom.ORCID https://orcid.org/0009-0004-6962-2008

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundClinical decision support systems (CDSSs) are designed to assist in health care delivery by supporting medical practice with clinical knowledge, patient information, and other relevant types of health information. CDSSs are integral parts of health care technologies assisting in disease management, including diagnosis, treatment, and monitoring. While electronic medical records (EMRs) serve as data repositories, CDSSs are used to assist clinicians in providing personalized, context-specific recommendations derived by comparing individual patient data to evidence-based guidelines.

objectiveThis targeted literature review (TLR) aimed to identify characteristics and features of both stand-alone and EMR-integrated CDSSs that influence their outcomes and benefits based on published scientific literature.

methodsA TLR was conducted using the Embase, MEDLINE, and Cochrane databases to identify data on CDSSs published in a 10-year frame (2012-2022). Studies on computerized, guideline-based CDSSs used by health care practitioners with a focus on chronic disease areas and reporting outcomes for CDSS utilization were eligible for inclusion.

resultsA total of 49 publications were included in the TLR. Studies predominantly reported on EMR-integrated CDSSs (ie, connected to an EMR database; n=32, 65%). The implementation of CDSSs varied globally, with substantial utilization in the United States and within the domain of cardio-renal-metabolic diseases. CDSSs were found to positively impact "quality assurance" (n=35, 69%) and provide "clinical benefits" (n=20, 41%), compared to usual care. Among CDSS features, treatment guidance and flagging were consistently reported as the most frequent elements for enhancing health care, followed by risk level estimation, diagnosis, education, and data export. The effectiveness of a CDSS was evaluated most frequently in primary care settings (n=34, 69%) across cardio-renal-metabolic disease areas (n=32, 65%), especially in diabetes (n=13, 26%). Studies reported CDSSs to be commonly used by a mixed group (n=27, 55%) of users including physicians, specialists, nurses or nurse practitioners, and allied health care professionals.

conclusionsOverall, both EMR-integrated and stand-alone CDSSs showed positive results, suggesting their benefits to health care providers and potential for successful adoption. Flagging and treatment recommendation features were commonly used in CDSSs to improve patient care; other features such as risk level estimation, diagnosis, education, and data export were tailored to specific requirements and collectively contributed to the effectiveness of health care delivery. While this TLR demonstrated that both stand-alone and EMR-integrated CDSSs were successful in achieving clinical outcomes, the heterogeneity of included studies reflects the evolving nature of this research area, underscoring the need for further longitudinal studies to elucidate aspects that may impact their adoption in real-world scenarios.

Indexed as

chronic disease managementclinical decision support systemdigital healthelectronic health recordsmobile phonenoncommunicable diseasestargeted literature review

Identifiers

PMID39602213
PMCPMC11635333

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.