Recent notable scientific publications in the field of applied clinical informatics. #6

In this monthly digest of recently peer-reviewed publications in the area of applied clinical informatics, we start by covering notable clinical decision support articles.

Cross-sectional study of providers' responses to preventive care reminders during visits to outpatient primary care sites. Investigators sought to determine whether reminder response rates varied when primary care providers saw their own patients as compared to when they saw their partners’ patients. The resultant response rates to reminders were 28.7% for the providers’ own patients and 12.6% for the patients of the providers’ partners. Given the relatively low latter response rate, the conclusion was obvious: Preventive care reminders should target specific providers [1].

Point-of-care access to medical information is crucial. An article in JAMIA described an emergency medicine wiki linked to offline mobile applications and reported survey results after 6 years of constant development. The public wiki, https://www.wikem.org, had 7,250 pages, over 150 million visits, and 200,000 mobile installs for offline use. Residency program graduates (over 90%) reported using the wiki after residency as a resource that helped to improve their clinical efficiency [2].

An article in BMJ Quality & Safety concerned the effectiveness of computerized provider order entry systems (CPOE) combined with clinical decision support systems (CDSS) in preventing adverse drug events (ADE). Five systematic reviews were identified from the specialized Agency for Healthcare Research and Quality (AHRQ) Patient Safety Net database. The conclusion of the report was that the CPOE+CDSS did not appear to prevent clinical ADEs reliably [3].

Telemedicine is growing and telehealth centers are looking for sustainable best practice models. A study published in Telemedicine and e-Health compared the business models of 10 successful telehealth centers by interviewing key personnel in those centers. The findings were that five general approaches helped to sustain a telehealth center: grants, telehealth network membership fees, income from the provision of clinical services, per encounter charges, and operating as a cost center [4].

Another article in the journal analyzed almost 6,000 telemedicine visits in a VA hospital. The telemedicine service saved 145 miles and 142 min per visit. However the reduction in travel payments remained modest given the telemedicine volumes [5].

A survey study examined patient interest in telehealth visits with a model in which the patients supplied the hardware and Internet connectivity. With 38% respondents indicating that they were "very likely" to accept an invitation to see their provider via video, 28% indicating that they were "somewhat likely" to do the same, and almost 34% indicating that they were "not at all likely" to do the same, the conclusion was that most patients were likely to be accepting of telehealth care [6].

A JAMIA article synthesized the factors that influenced healthcare providers’ adoption of mobile health (m-health) applications. From the 33 articles that the study included, the following factors were considered important: usefulness and ease of use, design, technical concerns, cost, privacy and security issues, familiarity with the technology, risk-benefit assessment information, and communication functionality [7].

A randomized controlled trial used a wearable device over six weeks and SMS text-messaging prompts to increase physical activity in overweight and obese adults. It concluded that Fitbit One achieved a small increase in moderate-intensity to vigorous-intensity physical activity at follow-up and that the SMS-based physical activity prompts were insufficient for increasing PA beyond one week [8].

A technical paper was published in the Journal of Biomedical Informatics. The authors described distributed personal grid architecture that could be used for non-time-critical applications such as searching through 10 million patient records in 30 minutes [9].

An observational study evaluated the short-term patient outcomes of mortality, readmissions, and adverse safety events after the implementation of inpatient EHRs in 17 hospitals, which were compared to 399 control hospitals in the same region. Despite concerns that the implementation of EHRs might adversely impact patient care during the acute transition period, the study found no overall negative association of such implementation with short-term inpatient mortality, adverse safety events, or readmissions in the Medicare population [10].


1.          Weinfeld JM, Gorman PN. Primary Care Physician Designation and Response to Clinical Decision Support Reminders: A Cross-Sectional Study. Appl Clin Inform. 2016;7(2):248–59. doi:10.4338/ACI-2015-10-RA-0142.PMID: 27437038

2.          Donaldson RI, Ostermayer DG, Banuelos R, Singh M. Development and usage of wiki-based software for point-of-care emergency medical information. J Am Med Inform Assoc. 2016. doi:10.1093/jamia/ocw033.PMID: 27121610

3.          Ranji SR, Rennke S, Wachter RM. Computerised provider order entry combined with clinical decision support systems to improve medication safety: a narrative review. BMJ Qual Saf. 2014;23(9):773–80. doi:10.1136/bmjqs-2013-002165.PMID: 24728888

4.          Effertz G, Alverson DC, Dion D, et al. Sustaining and Expanding Telehealth: A Survey of Business Models from Selected Prominent U.S. Telehealth Centers. Telemed J E Health. 2016. doi:10.1089/tmj.2016.0023.PMID: 27483137

5.          Russo JE, McCool RR, Davies L. VA Telemedicine: An Analysis of Cost and Time Savings. Telemed J E Health. 2016;22(3):209–15. doi:10.1089/tmj.2015.0055.PMID: 26305666

6.          Gardner MR, Jenkins SM, O’Neil DA, Wood DL, Spurrier BR, Pruthi S. Perceptions of video-based appointments from the patient’s home: a patient survey. Telemed J E Health. 2015;21(4):281–5. doi:10.1089/tmj.2014.0037.PMID: 25166260

7.          Gagnon M-P, Ngangue P, Payne-Gagnon J, Desmartis M. m-Health adoption by healthcare professionals: a systematic review. J Am Med Inform Assoc. 2016;23(1):212–20. doi:10.1093/jamia/ocv052.PMID: 26078410

8.          Wang JB, Cadmus-Bertram LA, Natarajan L, et al. Wearable Sensor/Device (Fitbit One) and SMS Text-Messaging Prompts to Increase Physical Activity in Overweight and Obese Adults: A Randomized Controlled Trial. Telemed J E Health. 2015;21(10):782–92. doi:10.1089/tmj.2014.0176.PMID: 26431257

9.          Yasnoff WA. A secure and efficiently searchable health information architecture. J Biomed Inform. 2016;61:237–46. doi:10.1016/j.jbi.2016.04.004.PMID: 27109933

10.        Barnett ML, Mehrotra A, Jena AB. Adverse inpatient outcomes during the transition to a new electronic health record system: observational study. BMJ. 2016;354:i3835.PMID: 27471242


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/