In this digest of recent peer-reviewed publications in applied clinical informatics we cover notable articles concerning professional satisfaction and physician burnout.
More than 6,000 US physicians were surveyed regarding EMR, CPOE, and patients’ portal usage. Use of these technologies in multivariate analysis was statistically significant and associated with less satisfaction with time spent on clerical tasks and a higher risk of professional burnout (1). The study sought nurses’ perspectives on their satisfaction and productivity with the hospital information system (HIS); the cohort of users was 154 nurses. Responsiveness, ease of learning, sufficient support, and record completeness and accuracy were identified as high-priority attributes of HIS (2). Annals of Internal Medicine published a qualitative time motion study to describe how physician time is spent in ambulatory practice. The authors quantified proportions of time spent on four activities: direct clinical face time, EHR and desk work, administrative tasks, and other tasks. The results showed that during a typical office day, physicians spent 27% of their time on direct clinical face time with patients and 49.2% of their time on EHR and desk work. However, when physicians were in an exam room with a patient, they spent 37% of their time on EHR and desk work. The conclusion is that clinicians spend twice as much time on EMR than on direct patient contact (3).
Electronic medical records could supply rich clinical data for quality metrics development. The article “Is It Feasible to Use Electronic Health Records for Quality Measurement of Adolescent Care?” reported a survey of IT professional in 10 pediatric health-care organizations. The survey asked about the feasibility of capturing electronic data for quality measurements. Overall, the feasibility scores varied across centers from 34% to 85%. The study concluded that EMR can facilitate automatic quality metrics, but its feasibility varies (4). Availability of data leads to the development of prediction models. This important point of view was published in JAMA. Super-utilizers (patients who make frequent visits to the ED and psychiatric crisis centers) often have financial problems and untreated chronic diseases. As one EMR has labeled those patients, the controversy is an ethical problem. The authors also found that this indicator can lead to biased clinical judgment, and at minimum EMR “should do no harm” (5). Another peer-reviewed article reported a retrospective cohort study to determine if longitudinal historical data in EMR can be used to predict patients’ future risk of suicidal behavior. From the 20,000 patients used as the study population, the model achieved a sensitivity of 33%–45% and a specificity of 90%−95%, in 3–4 years on average in advance prediction of patients’ future suicidal behavior (6). As this sensitivity rate is suboptimal, the model needs to be refined before clinical implementation. A recent PubMed-cited journal published the article “Big Data,” which presented a new approach using statistics and machine learning for detecting and analyzing patients who have unexpected responses to treatment (7).
The first systematic review to synthesize research studies involving the use of smart watch devices for health care was published in Applied Clinical Informatics. Twenty-four articles were included in the analysis. Most articles focused on the chronically ill elderly and were published in 2015. No usability testing reported prior implementation. The conclusion of the systematic review is that, with the expansion of wearable technologies, rigorous research into their use in clinical settings is needed (8). As a reflection of this statement, JAMA published a randomized clinical trial on the effect of wearable technology combined with a lifestyle intervention on long-term weight loss. The two-year study enrolled 471 participants (75% completed the study), which were randomized to the standard intervention group with self-monitoring of diet and physical activity, and a group with a wearable device. The primary outcome of weight loss was measured over 24 months at six-month intervals. The mean weight loss was 5.9 kg in the standard group and 3.5 kg in the technology-enhanced group, with a statistically significant difference. The conclusion is that both groups had weight loss, but devices that monitor and provide feedback on physical activity may not offer an advantage over standard behavioral weight loss approaches (9).
The last publication in this monthly review concerns the problem of hospital communication. It is a pre-post evaluation of a single hospital that uses a commercial secured-messaging mobile application. The outcome metrics were product utilization and clinician perception. Residents, social workers, and clinical resource coordinators were the largest per-person users of this communication system. More than half of the messages were read within a minute. The system showed a statistically significant reduction of workflow disruptions by both nursing and physician survey respondents (10).
1. Shanafelt TD, Dyrbye LN, Sinsky C, Hasan O, Satele D, Sloan J, et al. Relationship Between Clerical Burden and Characteristics of the Electronic Environment With Physician Burnout and Professional Satisfaction. Mayo Clin Proc. 2016 Jul;91(7):836–48. PMID: 27313121
2. Cohen JF, Coleman E, Kangethe MJ. An importance-performance analysis of hospital information system attributes: A nurses’ perspective. Int J Med Inform. 2016 Feb;86:82–90. PMID: 26564330
3. Sinsky C, Colligan L, Li L, Prgomet M, Reynolds S, Goeders L, et al. Allocation of Physician Time in Ambulatory Practice: A Time and Motion Study in 4 Specialties. Ann Intern Med. 2016 Sep 6; PMID: 27595430
4. Gardner W, Morton S, Tinoco A, Scholle SH, Canan BD, Kelleher KJ. Is It Feasible to Use Electronic Health Records for Quality Measurement of Adolescent Care? J Healthc Qual. 38(3):164–74. PMID: 26042752
5. Joy M, Clement T, Sisti D. The Ethics of Behavioral Health Information Technology: Frequent Flyer Icons and Implicit Bias. JAMA. 2016 Sep 8; PMID: 27607056
6. Barak-Corren Y, Castro VM, Javitt S, Hoffnagle AG, Dai Y, Perlis RH, et al. Predicting Suicidal Behavior From Longitudinal Electronic Health Records. Am J Psychiatry. 2016 Sep 9;appiajp201616010077. PMID: 27609239
7. Ozery-Flato M, Ein-Dor L, Parush-Shear-Yashuv N, Aharonov R, Neuvirth H, Kohn MS, et al. Identifying and Investigating Unexpected Response to Treatment: A Diabetes Case Study. Big data. 2016 Sep;4(3):148–59. PMID: 27541627
8. Lu T-C, Fu C-M, Ma MH-M, Fang C-C, Turner AM. Healthcare Applications of Smart Watches. A Systematic Review. Appl Clin Inform. 2016;7(3):850–69. PMID: 27623763
9. Jakicic JM, Davis KK, Rogers RJ, King WC, Marcus MD, Helsel D, et al. Effect of Wearable Technology Combined With a Lifestyle Intervention on Long-term Weight Loss: The IDEA Randomized Clinical Trial. JAMA. 2016 Sep 20;316(11):1161–71. PMID: 27654602
10. Patel N, Siegler JE, Stromberg N, Ravitz N, Hanson CW. Perfect Storm of Inpatient Communication Needs and an Innovative Solution Utilizing Smartphones and Secured Messaging. Appl Clin Inform. 2016;7(3):777–89. PMID: 27530155
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/