Last month, a notable systematic review was published in the Journal of Biomedical Informatics. Authors reviewed scientific literature from 1996 concerning inefficient navigation in EMR, targeting four study aims. Twenty-one articles met inclusion criteria. Navigation between multiple EMR screens was frequently identified as a usability barrier. Standard terminology for describing EHR navigation is identified as a barrier to better EMR navigation1.
Another systematic review published in the Journal of Medical Internet Research included 48 scientific articles published over the past 10 years on the topic of personal health records. Authors reviewed taxonomy, current data types, related standards, input strategies, goals, functions, and architectures of the PHR and identified open questions and challenges2.
A position article by the American College of Physicians Ethics, Professionalism and Human Rights Committee focuses on EMRs and the patient-physician relationship and aspects of patient care, such as patient autonomy, privacy, and confidentiality, as well as professionalism, clinical reasoning, and training3.
Journal of Medical Systems recently published an article that outlined the cloud-based secure transmission mechanism in personal health records that could work for multiple users (like nurse aides, patients, and family members)4. Personal health are more widely accessible in recent years given the rise of smartphones and the Internet of Things (IoT).
Subspecialty EMR adoption usually draws less attention than academic medical centers, hospitals, and ambulatory care. Maternal and Child Health Journal published an article with the objective of evaluating the effects of EHR adoption during pregnancy and use on maternal and child health care utilization and health among pregnant mothers and their infants. The study included over 200,000 infants’ and mothers’ records during four years of EMR usage. Investigators compared women and infants who received care from providers who adopted and used EMR with those who received care from other providers who do not use EMR. Women who received prenatal care mainly from a provider who adopted and used EMR were more likely to have well-child visits. However, it is unclear if the results were affected by better documentation with EMR5.
Telemedicine services already prove to be effective, safe, and beneficial to different groups of patients. A controversial article published by researchers from a nonprofit policy analytics corporation estimated spending on acute respiratory illness based on commercial claim data. They estimated that 12 percent of visits are replaced by telemedicine services and 88 are new encounters. Annual spending per telehealth patient increased by $45. The conclusions are that direct-to-consumer telehealth may increase access by making care more convenient for certain patients, but it may also increase utilization and health care spending6.
The potential of big data is unlocked only when distributed information is connected. Global Health Action published an article about an experiment in Ghana to assess the feasibility of using fingerprint identification to link community data and hospital data in a rural African setting. In this particular experiment, fingerprinting was used to identify hospital patients and was successful in 65% of cases. No concerns were expressed by community members about the fingerprint registration and identification processes7.
Adherence to medications could potentially be improved using mobile applications. Authors of a review published in the journal Telemedicine and e-Health identified 30 English-language medication-related applications. The top five were discussed in detail. Existing mobile applications have ideal features that could help patients take medication as prescribed. However, future research is required to study their efficacy8.
Manual calculation of clinical scores is a task that could be automatized using informatics tools. One hundred seventy-six readily available calculators are used in hospital medicine by six primary specialties, and 40 subspecialties were identified in an article published in Computer Methods and Programs in Biomedicine. A combination of survey results, online resources, log files, statistics, and clinician opinion identified the 13 most utilized calculators that could be candidates for further automatization9.
An article published in Applied Clinical Informatics reported the amount of time and the number of clicks saved to calculate cardiovascular risk and provide a treatment recommendation with and without a computerized clinical decision support tool. The results showed 3 minutes and 38 seconds saving per case as accuracy improved from the baseline of 60.61% to 100%10.
1. Roman LC, Ancker JS, Johnson SB, Senathirajah Y. Navigation in the electronic health record: A review of the safety and usability literature. J Biomed Inform. 2017;67:69-79. PMID:28088527.
2. Roehrs A, da Costa CA, Righi R da R, de Oliveira KSF. Personal Health Records: A Systematic Literature Review. J Med Internet Res. 2017;19(1):e13. PMID:28062391.
3. Sulmasy LS, López AM, Horwitch CA, , American College of Physicians Ethics P and HRC. Ethical Implications of the Electronic Health Record: In the Service of the Patient. J Gen Intern Med. March 2017. PMID:28321550.
4. Chen S-W, Chiang DL, Liu C-H, et al. Confidentiality Protection of Digital Health Records in Cloud Computing. J Med Syst. 2016;40(5):124. PMID:27059737.
5. Meghea CI, Corser W, You Z. Electronic Medical Record Use and Maternal and Child Care and Health. Matern Child Health J. 2016;20(4):819-826. PMID:26676978.
6. Ashwood JS, Mehrotra A, Cowling D, Uscher-Pines L. Direct-To-Consumer Telehealth May Increase Access To Care But Does Not Decrease Spending. Health Aff (Millwood). 2017;36(3):485-491. PMID:28264950.
7. Odei-Lartey EO, Boateng D, Danso S, et al. The application of a biometric identification technique for linking community and hospital data in rural Ghana. Glob Health Action. 2016;9:29854. PMID:26993473.
8. Haase J, Farris KB, Dorsch MP. Mobile Applications to Improve Medication Adherence. Telemed J E Health. 2017;23(2):75-79. PMID:27248315.
9. Dziadzko MA, Gajic O, Pickering BW, Herasevich V. Clinical calculators in hospital medicine: Availability, classification, and needs. Comput Methods Programs Biomed. 2016;133:1-6. PMID:27393794.
10. Scheitel MR, Kessler ME, Shellum JL, et al. Effect of a Novel Clinical Decision Support Tool on the Efficiency and Accuracy of Treatment Recommendations for Cholesterol Management. Appl Clin Inform. 2017;8(1):124-136. PMID:28174820.
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 75 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 medical informatics classes to medical school and master students.
He is active within informatics and professional societies serving number of committees.
More information at profile web page - http://www.mayoclinic.org/biographies/herasevich-vitaly-m-d-ph-d/bio-20055468