In this monthly digest of recently peer-reviewed publications in the area of applied clinical informatics, we started with covering notable review articles. The systematic review and meta-analysis titled “The Effectiveness of Information Technology to Improve Antimicrobial Prescribing in Hospitals” reviews over 50 years of evidence of the effectiveness of IT interventions to improve antimicrobial prescribing in hospitals. Of a total of 45 articles included in the analysis, IT interventions increased the appropriate use of antimicrobials (pooled RR: 1.49, 95% CI: 1.07–2.08); however, highly methodologically sound studies alone showed no effect on the outcome. Also, there was little evidence of the effect of IT interventions on patient mortality or length of he stay (LOS)1. Other reviews aimed to report the state of the science of Clinical Decision Support Systems (CDSS) for hospital bedside nurses. Only 28 articles met the inclusion criteria. The majority of the studies (80%) were single-site and either descriptive, qualitative, or quasi-experimental. Only one randomized trial was reported, and three studies had statistical procedures to detect changes2. As both articles show, there is a significant lack of scientifically and methodologically sound studies in the area of clinical informatics.
However, recent studies more and more are using methods of clinical research and health IT evaluation principals. A publication in Applied Clinical Informatics had the objective to develop, implement, and evaluate site-specific groupings of chief complaints (CC) that accurately identify children with head trauma. In this 13-site clinical trial comparing cranial computed tomography use before and after implementation of CDSS, the authors found that due to variability in CC across study sites, identical groupings were not possible. The paper reported over 90% sensitivity and up to 90% specificity in some sites3.
A prospective, observational cohort study of the impact of Computerized physician order entry (CPOE) analgesia-sedation protocols on adult patients receiving mechanical ventilation, requiring intravenous infusion of analgesics and/or sedatives, and expected to stay in the intensive care unit (ICU) ≥24 hours was published in BMC Anesthesiology. The CPOE of a sedation protocol was not associated with improved sedation practice or outcomes in critically ill patients but was associated with unpredicted increases of an analgesic dose. However, the revised CPOE protocol (age, kidney, and liver function adjusted) was associated with improved sedation practices4.
CDSS downtime and malfunctioning are still concerns. A group of investigators identified and studied several CDSS malfunctions at Brigham and Women’s Hospital. They also surveyed CMIO to assess the frequency of events. The authors concluded that CDSS malfunctions are common and often go undetected. Better methods are needed to prevent and detect these malfunctions5.
An interesting review, “Clinical Decision Support Tools: The Evolution of a Revolution,” was published in the Clinical Pharmacology & Therapeutics journal. The history and future development of modern dashboard systems for therapeutic drug monitoring (TDM) was described6.
Cognitive aids (CAs) such as emergency manuals and checklists are used in high-stress, time-sensitive situations. In a simulation study, anesthesia trainees used paper or electronic versions of Society for Pediatric Anesthesia emergency checklist. As post-study survey shows that 58% of trainees preferred the paper version and 35% preferred the electronic version7.
The evaluation study was published in the Health Care Management Review journal. Authors examined which IT system is better—a homegrown one or an outsourced one—and also the critical role of in-house IT expertise in the HIT implementation. The findings and conclusions were that the quality of patient care was significantly higher in hospitals deploying a homegrown HIT system than in hospitals deploying an outsourced HIT system8.
The Journal of Rural Health published a study examining the difference between rural and urban hospitals to identify other key factors and the overall level of readiness for Stage 2 meaningful use (MU) of EHR. A cross-sectional multivariate analysis using 2,083 samples from the 2013 HIMSS Analytics survey showed that rural hospitals were less likely to be ready for stage 2 MU compared to urban hospitals in the USA (OR = 0.49) due to increasingly limited resources9.
The last article in this month’s digest is titled “Identifying Home Care Clinicians' Information Needs for Managing Fall Risks”10. This is a good addition to a series of articles that identify the information needs of clinicians in different settings and sceneries.
1. Baysari MT, Lehnbom EC, Li L, Hargreaves A, Day RO, Westbrook JI. The effectiveness of information technology to improve antimicrobial prescribing in hospitals: A systematic review and meta-analysis. Int J Med Inform. 2016;92:15–34. PMID: 27318068
2. Dunn Lopez K, Gephart SM, Raszewski R, Sousa V, Shehorn LE, Abraham J. Integrative review of clinical decision support for registered nurses in acute care settings. J Am Med Inform Assoc. 2016. PMID: 27330074
3. Deakyne SJ, Bajaj L, Hoffman J, et al. Development, Evaluation and Implementation of Chief Complaint Groupings to Activate Data Collection: A Multi-Center Study of Clinical Decision Support for Children with Head Trauma. Appl Clin Inform. 2015;6(3):521–35. PMID: 26448796
4. Haddad SH, Gonzales CB, Deeb AM, et al. Computerized physician order entry of a sedation protocol is not associated with improved sedation practice or outcomes in critically ill patients. BMC Anesthesiol. 2015;15:177. PMID: 26644114
5. Wright A, Hickman T-TT, McEvoy D, et al. Analysis of clinical decision support system malfunctions: a case series and survey. J Am Med Inform Assoc. 2016. PMID: 27026616
6. Mould DR, D’Haens G, Upton RN. Clinical Decision Support Tools: The Evolution of a Revolution. Clin Pharmacol Ther. 2016;99(4):405–18. PMID: 26785109
7. Watkins SC, Anders S, Clebone A, et al. Paper or plastic? Simulation based evaluation of two versions of a cognitive aid for managing pediatric peri-operative critical events by anesthesia trainees: evaluation of the society for pediatric anesthesia emergency checklist. J Clin Monit Comput. 2016;30(3):275–83. PMID: 26067401
8. Khatri N, Gupta V. Effective implementation of health information technologies in U.S. hospitals. Health Care Manage Rev. 41(1):11–21. PMID: 25120194
9. Kim J, Ohsfeldt RL, Gamm LD, Radcliff TA, Jiang L. Hospital Characteristics are Associated With Readiness to Attain Stage 2 Meaningful Use of Electronic Health Records. J Rural Health. 2016. PMID: 27424940
10. Alhuwail D, Koru G. Identifying Home Care Clinicians’ Information Needs for Managing Fall Risks. Appl Clin Inform. 2016;7(2):211–26. PMID: 27437035
About the Contributor
Vitaly Herasevich, MD, PhD, MSc, FCCM, CPHIMS is Associate Professor of Anesthesiology and Medicine in Department of Anesthesiology at Mayo Clinic. His interest in the area of medical informatics extends back to 1995 with specific concentration on the applied clinical informatics in critical care and science of healthcare delivery. Dr. Herasevich has interest in studying and development clinical syndromic surveillance alerting systems ("sniffers"), clinical data visualization (novel patient-centered EMR) and complex large data warehousing for healthcare predictive and prescriptive analytics as well as outcome reporting. He is author of more than 60 Pubmed cited articles and wrote two editions of book "Computer for Physician". As a part of education effort Dr. Herasevich serves Clinical Informatics Fellowship program as Associate Program Director, appointed with full faculty privileges in Mayo Graduate School and teaching class “Health Information Technology evaluation”. He is active within informatics and professional societies serving number of committees.
More information at lab web page - http://www.mayo.edu/research/labs/clinical-informatics-intensive-care/