Effects of computerized decision support system implementations on patient outcomes in inpatient care: a systematic review

Varghese J, Kleine M, Gessner SI, Sandmann S, Dugas M (2018)
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 25(5): 593-602.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
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Autor*in
Varghese, Julian; Kleine, MarenUniBi; Gessner, Sophia Isabella; Sandmann, Sarah; Dugas, Martin
Abstract / Bemerkung
Objectives: To systematically classify the clinical impact of computerized clinical decision support systems (CDSSs) in inpatient care. Materials and Methods: Medline, Cochrane Trials, and Cochrane Reviews were searched for CDSS studies that assessed patient outcomes in inpatient settings. For each study, 2 physicians independently mapped patient outcome effects to a predefined medical effect score to assess the clinical impact of reported outcome effects. Disagreements were measured by using weighted kappa and solved by consensus. An example set of promising disease entities was generated based on medical effect scores and risk of bias assessment. To summarize technical characteristics of the systems, reported input variables and algorithm types were extracted as well. Results: Seventy studies were included. Five (7%) reported reduced mortality, 16 (23%) reduced life-threatening events, and 28 (40%) reduced non-life-threatening events, 20 (29%) had no significant impact on patient outcomes, and 1 showed a negative effect (weighted kappa: 0.72, P<.001). Six of 24 disease entity settings showed high effect scores with medium or low risk of bias: blood glucose management, blood transfusion management, physiologic deterioration prevention, pressure ulcer prevention, acute kidney injury prevention, and venous thromboembolism prophylaxis. Most of the implemented algorithms (72%) were rule-based. Reported input variables are shared as standardized models on a metadata repository. Discussion and Conclusion: Most of the included CDSS studies were associated with positive patient outcomes effects but with substantial differences regarding the clinical impact. A subset of 6 disease entities could be filtered in which CDSS should be given special consideration at sites where computer-assisted decision-making is deemed to be underutilized.
Stichworte
clinical decision support systems; medical order entry systems; reminder; systems; outcome and process assessment
Erscheinungsjahr
2018
Zeitschriftentitel
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
Band
25
Ausgabe
5
Seite(n)
593-602
ISSN
1067-5027
eISSN
1527-974X
Page URI
https://pub.uni-bielefeld.de/record/2920975

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Varghese J, Kleine M, Gessner SI, Sandmann S, Dugas M. Effects of computerized decision support system implementations on patient outcomes in inpatient care: a systematic review. JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION. 2018;25(5):593-602.
Varghese, J., Kleine, M., Gessner, S. I., Sandmann, S., & Dugas, M. (2018). Effects of computerized decision support system implementations on patient outcomes in inpatient care: a systematic review. JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION, 25(5), 593-602. doi:10.1093/jamia/ocx100
Varghese, J., Kleine, M., Gessner, S. I., Sandmann, S., and Dugas, M. (2018). Effects of computerized decision support system implementations on patient outcomes in inpatient care: a systematic review. JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 25, 593-602.
Varghese, J., et al., 2018. Effects of computerized decision support system implementations on patient outcomes in inpatient care: a systematic review. JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION, 25(5), p 593-602.
J. Varghese, et al., “Effects of computerized decision support system implementations on patient outcomes in inpatient care: a systematic review”, JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION, vol. 25, 2018, pp. 593-602.
Varghese, J., Kleine, M., Gessner, S.I., Sandmann, S., Dugas, M.: Effects of computerized decision support system implementations on patient outcomes in inpatient care: a systematic review. JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION. 25, 593-602 (2018).
Varghese, Julian, Kleine, Maren, Gessner, Sophia Isabella, Sandmann, Sarah, and Dugas, Martin. “Effects of computerized decision support system implementations on patient outcomes in inpatient care: a systematic review”. JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 25.5 (2018): 593-602.

3 Zitationen in Europe PMC

Daten bereitgestellt von Europe PubMed Central.

A Human(e) Factor in Clinical Decision Support Systems.
Bezemer T, de Groot MC, Blasse E, Ten Berg MJ, Kappen TH, Bredenoord AL, van Solinge WW, Hoefer IE, Haitjema S., J Med Internet Res 21(3), 2019
PMID: 30888324
[Electronic decision support to promote medication safety].
Haefeli WE, Seidling HM., Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 61(3), 2018
PMID: 29340732
Predicting Low Information Laboratory Diagnostic Tests.
Roy SK, Hom J, Mackey L, Shah N, Chen JH., AMIA Jt Summits Transl Sci Proc 2017(), 2018
PMID: 29888076

69 References

Daten bereitgestellt von Europe PubMed Central.

Effects of health information technology on patient outcomes: a systematic review.
Brenner SK, Kaushal R, Grinspan Z, Joyce C, Kim I, Allard RJ, Delgado D, Abramson EL., J Am Med Inform Assoc 23(5), 2015
PMID: 26568607
Effect of clinical decision-support systems: a systematic review.
Bright TJ, Wong A, Dhurjati R, Bristow E, Bastian L, Coeytaux RR, Samsa G, Hasselblad V, Williams JW, Musty MD, Wing L, Kendrick AS, Sanders GD, Lobach D., Ann. Intern. Med. 157(1), 2012
PMID: 22751758
Reasons or excuses for avoiding meta-analysis in forest plots.
Ioannidis JP, Patsopoulos NA, Rothstein HR., BMJ 336(7658), 2008
PMID: 18566080
Effectiveness of computerized decision support systems linked to electronic health records: a systematic review and meta-analysis.
Moja L, Kwag KH, Lytras T, Bertizzolo L, Brandt L, Pecoraro V, Rigon G, Vaona A, Ruggiero F, Mangia M, Iorio A, Kunnamo I, Bonovas S., Am J Public Health 104(12), 2014
PMID: 25322302
Features of effective computerised clinical decision support systems: meta-regression of 162 randomised trials.
Roshanov PS, Fernandes N, Wilczynski JM, Hemens BJ, You JJ, Handler SM, Nieuwlaat R, Souza NM, Beyene J, Van Spall HG, Garg AX, Haynes RB., BMJ 346(), 2013
PMID: 23412440
Health information technology: an updated systematic review with a focus on meaningful use.
Jones SS, Rudin RS, Perry T, Shekelle PG., Ann. Intern. Med. 160(1), 2014
PMID: 24573664
Incidence of venous thromboembolism verified by necropsy over 30 years.
Lindblad B, Sternby NH, Bergqvist D., BMJ 302(6778), 1991
PMID: 2021744
Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement.
Moher D, Liberati A, Tetzlaff J, Altman DG; PRISMA Group, Altman D, Antes G, Atkins D, Barbour V, Barrowman N, Berlin JA, Clark J, Clarke M, Cook D, D'Amico R, Deeks JJ, Devereaux PJ, Dickersin K, Egger M, Ernst E, Gotzsche PC, Grimshaw J, Guyatt G, Higgins J, Ioannidis JP, Kleijnen J, Lang T, Liberati A, Magrini N, McNamee D, Moja L, Moher D, Mulrow C, Napoli M, Oxman A, Pham B, Rennie D, Sampson M, Schulz KF, Shekelle PG, Tetzlaff J, Tovey D, Tugwell P., Ann. Intern. Med. 151(4), 2009
PMID: 19622511
Effect of clinical decision support systems on patient outcomes in inpatient settings: a systematic review
Varghese, PROSPERO (), 2016

Sarkar, 2015

Hoerbst, 2016

AUTHOR UNKNOWN, 2011

Clinical, 0
Portal of medical data models: information infrastructure for medical research and healthcare.
Dugas M, Neuhaus P, Meidt A, Doods J, Storck M, Bruland P, Varghese J., Database (Oxford) 2016(), 2016
PMID: 26868052

Yu, 2003
Comparison of rule-based and Bayesian network approaches in medical diagnostic systems
Oniésko, Artificial Intell Med. 2101(), 2001
Probable networks and plausible predictions – a review of practical Bayesian methods for supervised neural networks
DJV, Network: Comput Neural Syst. 6(3), 2009

Institute, 0
Interrater reliability: the kappa statistic.
McHugh ML., Biochem Med (Zagreb) 22(3), 2012
PMID: 23092060
Implementation of a tight glycaemic control protocol using a web-based insulin dose calculator.
Thomas AN, Marchant AE, Ogden MC, Collin S., Anaesthesia 60(11), 2005
PMID: 16229694
Intensive insulin therapy: enhanced Model Predictive Control algorithm versus standard care.
Cordingley JJ, Vlasselaers D, Dormand NC, Wouters PJ, Squire SD, Chassin LJ, Wilinska ME, Morgan CJ, Hovorka R, Van den Berghe G., Intensive Care Med 35(1), 2008
PMID: 18661120
A randomized study in diabetic patients undergoing cardiac surgery comparing computer-guided glucose management with a standard sliding scale protocol.
Saager L, Collins GL, Burnside B, Tymkew H, Zhang L, Jacobsohn E, Avidan M., J. Cardiothorac. Vasc. Anesth. 22(3), 2007
PMID: 18503924
Computerized physician order entry- based hyperglycemia inpatient protocol and glycemic outcomes: The CPOE-HIP study.
Guerra YS, Das K, Antonopoulos P, Borkowsky S, Fogelfeld L, Gordon MJ, Palal BM, Witsil JC, Lacuesta EA., Endocr Pract 16(3), 2010
PMID: 20061296
Impact of a computer-generated alert system on the quality of tight glycemic control.
Meyfroidt G, Wouters P, De Becker W, Cottem D, Van den Berghe G., Intensive Care Med 37(7), 2011
PMID: 21369814
Impact of an alerting clinical decision support system for glucose control on protocol compliance and glycemic control in the intensive cardiac care unit.
Lipton JA, Barendse RJ, Schinkel AF, Akkerhuis KM, Simoons ML, Sijbrands EJ., Diabetes Technol. Ther. 13(3), 2011
PMID: 21291336
Effect of a computerized insulin dose calculator on the process of glycemic control.
Dumont C, Bourguignon C., Am. J. Crit. Care 21(2), 2012
PMID: 22381987
The effect of a computerized prescribing and calculating system on hypo- and hyperglycemias and on prescribing time efficiency in neonatal intensive care patients.
Maat B, Rademaker CM, Oostveen MI, Krediet TG, Egberts TC, Bollen CW., JPEN J Parenter Enteral Nutr 37(1), 2012
PMID: 22535919
Software-guided insulin dosing: tight glycemic control and decreased glycemic derangements in critically ill patients.
Saur NM, Kongable GL, Holewinski S, O'Brien K, Nasraway SA Jr., Mayo Clin. Proc. 88(9), 2013
PMID: 24001484
Tight computerized versus conventional glucose control in the ICU: a randomized controlled trial.
Kalfon P, Giraudeau B, Ichai C, Guerrini A, Brechot N, Cinotti R, Dequin PF, Riu-Poulenc B, Montravers P, Annane D, Dupont H, Sorine M, Riou B; CGAO-REA Study Group., Intensive Care Med 40(2), 2014
PMID: 24420499
Intraoperative blood glucose management: impact of a real-time decision support system on adherence to institutional protocol.
Nair BG, Grunzweig K, Peterson GN, Horibe M, Neradilek MB, Newman SF, Van Norman G, Schwid HA, Hao W, Hirsch IB, Patchen Dellinger E., J Clin Monit Comput 30(3), 2015
PMID: 26067402
Impact of a hypoglycemia reduction bundle and a systems approach to inpatient glycemic management.
Maynard G, Kulasa K, Ramos P, Childers D, Clay B, Sebasky M, Fink E, Field A, Renvall M, Juang PS, Choe C, Pearson D, Serences B, Lohnes S., Endocr Pract 21(4), 2014
PMID: 25536971
Computerized physician order entry improves compliance with a manual exchange transfusion protocol in the pediatric intensive care unit.
McCrory MC, Strouse JJ, Takemoto CM, Easley RB., J. Pediatr. Hematol. Oncol. 36(2), 2014
PMID: 23619120
Restrictive blood transfusion practices are associated with improved patient outcomes.
Goodnough LT, Maggio P, Hadhazy E, Shieh L, Hernandez-Boussard T, Khari P, Shah N., Transfusion 54(10 Pt 2), 2014
PMID: 24995770
Reduced red blood cell transfusion in cardiothoracic surgery after implementation of a novel clinical decision support tool.
Razavi SA, Carter AB, Puskas JD, Gregg SR, Aziz IF, Buchman TG., J. Am. Coll. Surg. 219(5), 2014
PMID: 25026877
A Patient Blood Management Program in Prosthetic Joint Arthroplasty Decreases Blood Use and Improves Outcomes.
Loftus TJ, Spratling L, Stone BA, Xiao L, Jacofsky DJ., J Arthroplasty 31(1), 2015
PMID: 26346704
Automated detection of physiologic deterioration in hospitalized patients.
Evans RS, Kuttler KG, Simpson KJ, Howe S, Crossno PF, Johnson KV, Schreiner MN, Lloyd JF, Tettelbach WH, Keddington RK, Tanner A, Wilde C, Clemmer TP., J Am Med Inform Assoc 22(2), 2014
PMID: 25164256
Impact of introducing an electronic physiological surveillance system on hospital mortality.
Schmidt PE, Meredith P, Prytherch DR, Watson D, Watson V, Killen RM, Greengross P, Mohammed MA, Smith GB., BMJ Qual Saf 24(1), 2014
PMID: 25249636
Using EHR data to predict hospital-acquired pressure ulcers: a prospective study of a Bayesian Network model.
Cho I, Park I, Kim E, Lee E, Bates DW., Int J Med Inform 82(11), 2013
PMID: 23891086
Reduction in the incidence of pressure ulcers upon implementation of a reminder system for health-care providers.
Sebastian-Viana T, Losa-Iglesias M, Gonzalez-Ruiz JM, Lema-Lorenzo I, Nunez-Crespo FJ, Salvadores Fuentes P; ARCE team, Sebastian-Viana T, Gonzalez-Ruiz JM, Nunez-Crespo FJ, Lema-Lorenzo I, Martin-Merino G, Garcia-Martin MR, Velayos-Rodriguez E, Nogueiras-Quintas CG, Serrano Balazote P, Lechuga-Suarez L, Navalon-Cebrian R, Medino-Munoz J., Appl Nurs Res 29(), 2015
PMID: 26856498
Effect of an electronic alert on risk of contrast-induced acute kidney injury in hospitalized patients undergoing computed tomography.
Cho A, Lee JE, Yoon JY, Jang HR, Huh W, Kim YG, Kim DJ, Oh HY., Am. J. Kidney Dis. 60(1), 2012
PMID: 22497793
Impact of vendor computerized physician order entry on patients with renal impairment in community hospitals.
Leung AA, Schiff G, Keohane C, Amato M, Simon SR, Cadet B, Coffey M, Kaufman N, Zimlichman E, Seger DL, Yoon C, Bates DW., J Hosp Med 8(10), 2013
PMID: 24101539
Electronic alerts to prevent venous thromboembolism among hospitalized patients.
Kucher N, Koo S, Quiroz R, Cooper JM, Paterno MD, Soukonnikov B, Goldhaber SZ., N. Engl. J. Med. 352(10), 2005
PMID: 15758007
Maintained effectiveness of an electronic alert system to prevent venous thromboembolism among hospitalized patients.
Lecumberri R, Marques M, Diaz-Navarlaz MT, Panizo E, Toledo J, Garcia-Mouriz A, Paramo JA., Thromb. Haemost. 100(4), 2008
PMID: 18841295
Effects of clinical decision support on venous thromboembolism risk assessment, prophylaxis, and prevention at a university teaching hospital.
Galanter WL, Thambi M, Rosencranz H, Shah B, Falck S, Lin FJ, Nutescu E, Lambert B., Am J Health Syst Pharm 67(15), 2010
PMID: 20651317
Prevention of thromboembolic events in surgical patients through the creation and implementation of a computerized risk assessment program.
Novis SJ, Havelka GE, Ostrowski D, Levin B, Blum-Eisa L, Prystowsky JB, Kibbe MR., J. Vasc. Surg. 51(3), 2010
PMID: 20022209
Optimizing prevention of hospital-acquired venous thromboembolism (VTE): prospective validation of a VTE risk assessment model.
Maynard GA, Morris TA, Jenkins IH, Stone S, Lee J, Renvall M, Fink E, Schoenhaus R., J Hosp Med 5(1), 2010
PMID: 19753640
Improved prophylaxis and decreased rates of preventable harm with the use of a mandatory computerized clinical decision support tool for prophylaxis for venous thromboembolism in trauma.
Haut ER, Lau BD, Kraenzlin FS, Hobson DB, Kraus PS, Carolan HT, Haider AH, Holzmueller CG, Efron DT, Pronovost PJ, Streiff MB., Arch Surg 147(10), 2012
PMID: 23070407
Impact of a venous thromboembolism prophylaxis "smart order set": Improved compliance, fewer events.
Zeidan AM, Streiff MB, Lau BD, Ahmed SR, Kraus PS, Hobson DB, Carolan H, Lambrianidi C, Horn PB, Shermock KM, Tinoco G, Siddiqui S, Haut ER., Am. J. Hematol. 88(7), 2013
PMID: 23553743
Impact of electronic reminders on venous thromboprophylaxis after admissions and transfers.
Beeler PE, Eschmann E, Schumacher A, Studt JD, Amann-Vesti B, Blaser J., J Am Med Inform Assoc 21(e2), 2014
PMID: 24671361
Decision support systems in clinical practice: The case of venous thromboembolism prevention.
Nazarenko GI, Kleymenova EB, Payushik SA, Otdelenov VA, Sychev DA, Yashina LP., Int J Risk Saf Med 27 Suppl 1(), 2015
PMID: 26639683
Computerized clinical decision support improves warfarin management and decreases recurrent venous thromboembolism.
Woller SC, Stevens SM, Towner S, Olson J, Christensen P, Hamilton S, Newman L, Mott L, Hu P, Brunisholz KD, Long Y, Lloyd J, Evans RS, Cannon W, Elliott CG., Clin. Appl. Thromb. Hemost. 21(3), 2014
PMID: 25228672

European, 2012
FDA regulation of clinical decision support software.
Karnik K., J Law Biosci 1(2), 2014
PMID: 27774161
Grand challenges in clinical decision support.
Sittig DF, Wright A, Osheroff JA, Middleton B, Teich JM, Ash JS, Campbell E, Bates DW., J Biomed Inform 41(2), 2007
PMID: 18029232
Clinical decision support system for diagnosis and management of chronic renal failure
Al-Hyari, 2013
Optimizing Clinical Decision Support in the Electronic Health Record. Clinical Characteristics Associated with the Use of a Decision Tool for Disposition of ED Patients with Pulmonary Embolism.
Ballard DW, Vemula R, Chettipally UK, Kene MV, Mark DG, Elms AK, Lin JS, Reed ME, Huang J, Rauchwerger AS, Vinson DR; KP CREST Network Investigators., Appl Clin Inform 7(3), 2016
PMID: 27652375
Publication bias in clinical trials due to statistical significance or direction of trial results
Hopewell, Cochrane Database Syst Rev 21(1), 2009

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