The DOI matches its Crossref record; 121 of the 122 references it cites were found in a registry or an index, and 1 are not checked yet.
Harnessing the power of clinical decision support systems: challenges and opportunities
Open accessAbstract
Clinical decision support systems (CDSSs) are increasingly integrated into healthcare settings to improve patient outcomes, reduce medical errors and enhance clinical efficiency by providing clinicians with evidence-based recommendations at the point of care. However, the adoption and optimisation of these systems remain a challenge. This review aims to provide an overview of the current state of CDSS, discussing their development, implementation, benefits, limitations and future directions. We also explore the potential for enhancing their effectiveness and provide an outlook for future developments in this field. There are several challenges in CDSS implementation, including data privacy concerns, system integration and clinician acceptance. While CDSS have demonstrated significant potential, their adoption and optimisation remain a challenge.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Figures
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“CDSS are software tools designed to help healthcare professionals make clinical decisions by integrating patient-specific information with a knowledge database using advanced algorithms to deliver customized recommendations or assessments in real time [211].”
From Toward Safer Diagnoses: A SEIPS-Based Narrative Review of Diagnostic Errors (2026) · cites this paper as [211]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
High-performance medicine: the convergence of human and artificial intelligence
Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450
A New Initiative on Precision Medicine
Collins, F. S., & Varmus, H. (2015) · New England Journal of Medicine · doi:10.1056/nejmp1500523 · Cited by 5,202
Rajkomar, A., Dean, J., & Kohane, I. (2019) · New England Journal of Medicine · doi:10.1056/nejmra1814259 · Cited by 4,579
An overview of clinical decision support systems: Benefits, risks, and strategies for success
Sutton, R. T., Pincock, D., Baumgart, D. C., Sadowski, D. C., Fedorak, R. N., & Kroeker, K. I. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0221-y · Cited by 3,019
Improving Primary Care for Patients With Chronic Illness
Bodenheimer, T. (2002) · JAMA · doi:10.1001/jama.288.14.1775 · Cited by 2,961
Garg, A. X., Adhikari, N. K. J., McDonald, H., Rosas-Arellano, M. P., Devereaux, P. J., Beyene, J., Sam, J., & Haynes, R. B. (2005) · JAMA · doi:10.1001/jama.293.10.1223 · Cited by 2,936
Artificial intelligence in healthcare
Yu, K.-H., Beam, A. L., & Kohane, I. S. (2018) · Nature Biomedical Engineering · doi:10.1038/s41551-018-0305-z · Cited by 2,817
Kawamoto, K., Houlihan, C. A., Balas, E. A., & Lobach, D. F. (2005) · BMJ · doi:10.1136/bmj.38398.500764.8f · Cited by 2,703
Big Data and Machine Learning in Health Care
Beam, A. L., & Kohane, I. S. (2018) · JAMA · doi:10.1001/jama.2017.18391 · Cited by 2,506
The practical implementation of artificial intelligence technologies in medicine
He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X., & Zhang, K. (2019) · Nature Medicine · doi:10.1038/s41591-018-0307-0 · Cited by 2,246
Research works printed but not linked: 1
- Kim KM , Yefimova M , Lin FV , et al . A home-recovery surgical care model using AI-driven measures of activities of daily living. NEJM Catal Innov Care Deliv 2022;3. doi:10.1056/CAT.22.0081 · DOI
Cited by
Sorted by citations
Items 201–243 of 243
Clinical decision support to aid in diagnosis of heparin‐induced thrombocytopenia
Gallo, T., Heise, C. W., Curry, S. C., Antonescu, C., & Raschke, R. A. (2026) · British Journal of Haematology · doi:10.1111/bjh.70551
Toward an Intelligent Clinical Decision Support System in the Prehospital Setting
Aziz, O. F. A., Benhadou, S., & Maria, M. (2026) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-032-23035-5_12
Lepore, M., Maccioni, R., Plenzich, E., & Tufano, R. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-21811-7_20
Nananukul, N., & Kejriwal, M. (2026) · ACM Conference on AI and Agentic Systems (CAIS) · doi:10.1145/3786335.3813224
Prediction models for sarcopenia in older adults in China: a scoping review
Kanfei, Y., Xu, Y., Xu, J., Zhang, X., Gu, F., Hua, L., & Li, X. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1835309
Challenges and opportunities in ECMO monitoring: A mixed-methods appraisal
Yin, W., Permessur, A., Pittas, G., Sukhlal, S., Chow, N., Pinheiro, S., Cobos, A., Sabetta, G., Cahill, J., Zaidi, S., et al. (2026) · Journal of ExtraCorporeal Technology · doi:10.1051/ject/2026022
Babaei, S., Jebraeily, M., Hasanloei, M. A. V., & Nourani, A. (2026) · BMC Digital Health · doi:10.1186/s44247-026-00249-w
Expert Consensus on LLM Safety in Clinical Decision Support
Gayathri, K. V., Kanshiya, S., Saranraj, P., Lokeshwari, B., Vilmasen, P., & Wins, J. A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7862-6.ch011
Khosravi, M., Zare, Z., Mojtabaeian, S. M., & Izadi, R. (2026) · Health Science Reports · doi:10.1002/hsr2.72665
Şahin, R. S., Küçükdevlet, L. G., Korkmaz, B., İlhan, M. E., Yeni, A. B., Kurt, Z., Kudubeş, A. A., Gündoğdu, F., Tuna, A., Tayaz, E., et al. (2026) · BMC Nursing · doi:10.1186/s12912-026-04851-1
Rare disease monitoring plans: a case study within a clinical decision support system
Campos, L. N., Yazbek, R. I. B., Rivera, I. D., Rudzinski, I. V., Pintos, G. O., Curto, S., Maximowicz, S. M., Gerk, A., Zelcer, F. F., Stegmann, C., et al. (2026) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2026.111814
Ismail, M. A. (2026) · The Pharmacogenomics Journal · doi:10.1038/s41397-026-00422-4
Ломакин, А. С., & Makarov, A. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-30494-0_19
Context aware knowledge discovery for clinical support using transfer learning
Paliwal, G., & Bunglowala, A. (2026) · Multimedia Tools and Applications · doi:10.1007/s11042-026-21765-x
Kim, I., Robinson, T. N., Reeves, B. B., Haber, N., & Ram, N. (2026) · JMIR Formative Research · doi:10.2196/87320
Intelligent Decision Support Systems with Optimization for Clinical and Operational Healthcare
Ali, M. M. (2026) · International Conference on Computational Intelligence and Communication Networks (CICN) · doi:10.1109/cicn70047.2026.11594205
Chae, S., Harb, A., Youn, N., Annan, P. A. M., & Lopez, K. D. (2026) · Cancer Nursing · doi:10.1097/ncc.0000000000001606
Intelligent Teaming in Public Health Management
Manikantan, R., Shabu, J., Sruthi, A., Bhatt, A., Rajarathnam, D. R. P., & Keerthana, N. V. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-3280-0.ch007
Sharma, N., Singh, B., Bhardwaj, S., & Verma, R. (2026) · Cancer Research Statistics and Treatment · doi:10.4103/crst.crst_27_26
Dahò, M., Monzani, D., Coppini, V., Ferrari, M. V., Ferraris, G., Grasso, R., Galvão, V., Laurent, C., Machiavelli, A., Pietrobon, R., et al. (2026) · BMC Health Services Research · doi:10.1186/s12913-026-15210-w
Fanhani, F. C., Ragassi, W. M. D. C., Soutier, L. G., Hammerschmitt, B. K., Pontarolo, R., Mendes, A. E. M., & Ferreira, L. M. (2026) · European Journal of Pharmaceutics and Biopharmaceutics · doi:10.1016/j.ejpb.2026.115205
Machine Learning-Enhanced Clinical Decision Making in Dementia Care: A Systematic Review
Saxena, R. R., & Saxena, R. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-34459-5_16
Farina, J. M., Scalia, I. G., O'Connor, D. R., Gould, C. E., Salazar, B. R., Bibby, P. S., Cason, S., Fix, R. A., Chapital, A. B., Scott, L. R., et al. (2026) · Mayo Clinic Proceedings Digital Health · doi:10.1016/j.mcpdig.2026.100391
Transforming Care With AI-Enabled CDSS: A Survey of Innovations, Impact, and Open Problems
Geetha, R., & Shashikumar, D. R. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-26905-8_32
Søgaard, S. N., Ward, L., Mogensen, M. L., Mogensen, C. B., Cartuliares, M. B., & Skjøt‐Arkil, H. (2026) · JAC-Antimicrobial Resistance · doi:10.1093/jacamr/dlag160
Explainable AI for Clinical Oncology Decisions
Dey, N., Misra, B., Fong, S., Sherratt, R. S., & Shi, F. (2026) · International Conference on Intelligent Engineering and Next-Gen Healthcare Systems (IEHNS) · doi:10.1109/iehns68708.2026.11606796
Industry Practitioners Developing or Implementing AI Platforms for Healthcare and Life Sciences
Atul Kumar Dnyaan Prasad Global University, Pulatov, M., Rejabbayevich, N. D., Almat, S., Рустамович, Х. Т., & Mardonovich, N. A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-8904-2.ch017
Promise and Caution: Mapping Opportunities for AI Decision Support in Emergency Medical Services
Zhang, Z., Agbugba, V. F., Bai, E., Billings, S., Luo, X., & Adelgais, K. (2026) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi69079.2026.00086
Rehman, K., & Akash, M. S. H. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-45911-5.00007-6
ten Klooster, I., Kelders, S. M., Kip, H., Crutzen, R., & van Gemert‐Pijnen, J. E. W. C. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1773356 · Cited by 1
Renganathan, K., Haque, W., Singh, A., Elliott, M., Atiquzzaman, M., & Romann, A. (2026) · doi:10.1109/icoict71118.2026.11664773
Barış, V. K., Frost, A. C., Sainlaire, M., Chen, J., Shukla, M. V., Apurba, M. S. A., Hurley, A. C., & Dykes, P. C. (2026) · BMJ Open · doi:10.1136/bmjopen-2026-122559
Kama, M. O., Ajah, I. A., Ikegwu, A. C., Obayi, A. A., Uloko, F. O., & Ogbuagu, I. C. (2026) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-026-03796-4
Nascimento, H. D. O. (2026) · Nexus Science Review · doi:10.66104/4xwn8e04 · Cited by 1
Husby, H. M., Liu, J., Scott, A. B., Li, J., Huang, Q., Jones, J. B., & Yan, X. S. (2026) · Healthcare · doi:10.3390/healthcare14182937
Molla, M. H., Abualreesh, M., Alqahtani, M., Haridi, A., Khayat, M. F., Jahan, B., & Islam, M. S. (2026) · Oceans · doi:10.3390/oceans7050079
Disease Spectrum-aware and Time-evolving Dependency Learning for Medication Recommendation
Li, Y., Wang, C., Wang, S., Zhang, Q., Hu, L., Lian, D., Guo, L., & Lü, W. (2026) · ACM Transactions on Information Systems · doi:10.1145/3848522
Bounsall, K., Cong, C., Downey, J. A., Onyeachu, P., Haunton, V. J., Allgar, V. L., & Carroll, C. (2026) · BMJ Open · doi:10.1136/bmjopen-2026-116525
Machine learning models in clinical decision support systems: a scoping review
Innocent, D. C., Innocent, R. C., & Innocent, I. P. (2026) · Bulletin of the National Research Centre/Bulletin of the National Research Center · doi:10.1186/s42269-026-01502-y
REASON: A Hybrid Agentic AI Framework for Preventing Outpatient Claim Rejections in BPJS Kesehatan
Previanto, A., Anggreainy, M. S., Gunawan, A. A. S., & Isa, S. M. (2026) · International Conference on ICT for Smart Society (ICISS) · doi:10.1109/iciss71466.2026.11715125
Rubiera, M., Bendszus, M., Leker, R. R., Hilbert, A., Werren, I., López-Ramos, L. M., Ayesta, M., Nguyen, Q., Bonekamp, S., Sala, V., et al. (2026) · BMJ Open · doi:10.1136/bmjopen-2026-120511
McCord, E., Blackburn, J. L., Holmes, A. M., Apathy, N. C., Grout, R. W., Harle, C. A., & Mazurenko, O. (2026) · medRxiv · doi:10.64898/2026.10.07.26364970
Steinhauser, S. (2026) · Cross Cultural & Strategic Management · doi:10.1108/ccsm-07-2024-0162
Metadata
- Authors
- Chen, Z., Liang, N., Zhang, H., Li, H., Yang, Y., Zong, X., Chen, Y., Wang, Y., & Shi, N.
- Affiliations
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
- China Academy of Chinese Medical Sciences (CN)
- Corresponding author
- Wang, Yanping
- Shi, Nannan
- Venue
- Open Heart
- Publisher
- BMJ
- Published
- 2023-11
- Volume
- 10
- Issue
- 2
- Pages
- e002432
- ISSN
- 2053-3624 (ISSN-L)
- PMC
- PMC10685930
- OpenAlex
- W4389101129
- Cited by
- 244
- References
- 121
- Citations by year
- 2026: 89 · 2025: 120 · 2024: 35
- FWCI
- 9.56
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Open Heart (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- China Academy of Chinese Medical Sciences Independent Selection Project (Z0830)
- Institute of Basic Research in Clinical Medicine Independent Selection Project, China Academy of Chinese Medical Sciences (Z0830-1)
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Technology and Human Factors in Education and Health
- Keywords
- clinical decision support systems, evidence-based recommendations, medical errors, clinical efficiency, data privacy, system integration, clinician acceptance, point-of-care decision support, patient outcomes
- MeSH terms
- Delivery of Health Care, Humans, Decision Support Systems, Clinical
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
