Experiences of Health Care Providers Using a Mobile Medical Photography Application.

OBJECTIVE: To understand the ways in which providers use a mobile photography application integrated with the electronic health record (EHR) to facilitate clinical care, and the process outcomes that result from the application's use. METHODS: An e-mail survey was sent on November 13, 2017, to 1,463 health care providers at Mayo Clinic who had used an internally developed, EHR-integrated medical photography application. RESULTS: The survey was completed by 712 (49%) providers. Providers reported using the application on approximately 1 in 7 days spent in clinical practice. Median provider satisfaction with the use of the application (0-100 scale; higher numbers indicate favorable response) was 94 (interquartile range [IQR]: 74-100). Although the use for store-and-forward telemedicine was reported (22% often or frequently used the application to send photographs to a specialist for advice), the most common use was for clinical documentation (65% often or frequently used the application to supplement text-based notes with photographs, and 71% often or frequently used the application to take photographs for reference by a colleague who may see the patient in the future). Of the health care providers, 36% indicated that the application's use often or frequently expedited treatment. DISCUSSION: Health care providers reported using a mobile point-of-care medical photography application regularly in clinical practice and were generally satisfied with the application. CONCLUSION: Point-of-care medical photography using a secure mobile, EHR-integrated application has potential to become a new standard of care for clinical documentation and may facilitate continuity across the continuum of care with multiple providers who see a patient.

PhotoExam: adoption of an iOS-based clinical image capture application at Mayo Clinic.

BACKGROUND: Mayo Clinic developed an internal iOS-based, point-of-care clinical image capture application for clinicians. We aimed to assess the adoption and utilization of the application at Mayo Clinic. METHODS: Metadata of 22,784 photos of 6417 patients taken by 606 users over 8040 clinical encounters between 3/1/2015 and 10/31/2015 were analyzed. A random sample of photos from 100 clinical encounters was assessed for quality using a five-item rubric. Use of traditional medical photography services before and after application launch were compared. RESULTS: The largest group of users was residents/fellows, accounting for 31% of users but only 18% of all photos. Attending physicians accounted for 29% of users and 30% of photos. Nurses accounted for 14% of users and 28% of photos. Surgical specialties had the most users (36% of users), followed by dermatology (14% of users); however, dermatology accounted for 54% of all photos, and surgery accounted for 26% of photos. Images received an average of 91% of possible points on the quality scoring rubric. Most frequent reasons for missing points were the location on the body not clearly being demonstrated (19% of encounters) and the perspective/scale not being clearly demonstrated (12% of encounters). There was no discernible pre-post effect of the application's launch on use of traditional medical photography services. CONCLUSIONS: Point-of-care clinical photography is a growing phenomenon with potential to become the new standard of care. Patient and provider attitudes and the impact on patient outcomes remain unclear.

Using RxNorm and NDF-RT to classify medication data extracted from electronic health records: experiences from the Rochester Epidemiology Project.

RxNorm and NDF-RT published by the National Library of Medicine (NLM) and Veterans Affairs (VA), respectively, are two publicly available federal medication terminologies. In this study, we evaluate the applicability of RxNorm and National Drug File-Reference Terminology (NDF-RT) for extraction and classification of medication data retrieved using structured querying and natural language processing techniques from electronic health records at two different medical centers within the Rochester Epidemiology Project (REP). Specifically, we explore how mappings between RxNorm concept codes and NDF-RT drug classes can be leveraged for hierarchical organization and grouping of REP medication data, identify gaps and coverage issues, and analyze the recently released NLM's NDF-RT Web service API. Our study concludes that RxNorm and NDF-RT can be applied together for classification of medication extracted from multiple EHR systems, although several issues and challenges remain to be addressed. We further conclude that the Web service APIs developed by the NLM provide useful functionalities for such activities.