Personal health records (PHRs) are electronic medical records whose contents are controlled and managed by the consumers whose data they carry. PHRs have attracted much press attention in recent years, and both industry and governments in many countries see them as potential solutions to many IT problems in health care. The National Library of Medicine (NLM) is developing a PHR in order to study and improve PHR systems utility, reduce the barriers to their use, identify best practices, and provide a platform and test bed for advanced applications. The PHR structure is based on a set of existing messaging and vocabulary standards that are supported by the Department of Health and Human Services and the NLM, and its development is based on both publicly available open-source software (Ruby on Rails, Scriptaculous, and jQuery) and software developed at NLM which will be released as open-source, and could have broad applicability to electronic medical record software. The PHR is a web application that uses an internally developed forms generator with rule-based skip logic (e.g., if the person is male, it does not ask about pregnancy history), data validation checks and auto-complete input. It uses AJAX techniques to communicate with the server, so the response time is very fast. The NLM PHR can manage clinical information for many different family members under one account;for example, a mother can maintain immunization records for herself and each of her children, and also keep track of her ailing father's medications. Users can record medications, medical problems, surgeries, immunizations, and important measurements such as blood pressure and laboratory results (e.g., hemoglobin or cholesterol). The major clinical variables included in the PHR--e.g., drugs, problems, surgeries, immunizations are mapped to federally-supported standard coding systems: Drugs are mapped to RxTerms (a subset of RxNorm that was developed for this project, measurements are mapped to Logical Observation Identifiers Names and Codes (LOINC) also required by regulators, immunizations are mapped to the Centers for Disease Control's vaccine CVX codes, and diagnoses and procedures are mapped to Systematized Nomenclature of MedicineClinical Terms (SNOMED-CT). All of which are required for use in electronic messages by federal regulators. The PHR adopts Health Level 7 (HL7) standards for much of its data structure and all of its data types, which will facilitate the import and export of clinical data. Automatic coding in the NLM PHR provides three special capabilities: 1) custom clinical decision support;2) one-click access to health information about coded concepts entered in the PHR, and 3) identification of duplicate prescribing with different but equivalent drugs. Accomplishments and new features added in FY2013 include: o Capability to view previously entered data as well as enter new data points for screening tests on the main PHR page o Revision of the allergy and immunization tables to simplify user entry o Redesign of the health reminders to distinguish reminders that have been read from those that are new;addition of a visual cue to the main PHR page for the number of unread health reminders o Addition of new medical conditions and surgeries, as well as consumer names and synonyms for each term o Redesign of health panel organization and display o Addition of tooltips throughout the user interface o Launch of a demonstration site for those interested in the NLM PHR to test out the system without actually storing data o Formal usability testing;re-design of parts of the user interface in progress o Addition of several types of usage statistics;refinement of the data that are captured and specific reports to view the usage statistics are in progress o Updated software to Ruby 2.0 and Rails 4.0 o Updated JQuery from version 1.6 to 1.9 and implemented 1.9s datepicker, a specialized calendar function o In progress capability to import health data from a Continuity of Care Document (CCD) This project has been a longstanding NLM interest and is part of the NLM strategic plan. It uses and tunes the message and vocabulary standards that NLM has supported, and it will also provide another consumer entry point to NLM's rich trove of patient-oriented data. Early research projects will focus on users'needs, usability, and usage patterns to guide the next round of development and research. We are still in the process of finalizing agreements with Bethesda's Suburban Hospital (now a part of Johns Hopkins) in anticipation of Suburban Hospital installing and operating the NLM PHR as a service to people in the community and providing de-identified data to NLM for usability research and further software development.We are also exploring uses for patient data collection instrumental for clinical trials.
| Vreeman, Daniel J; Abhyankar, Swapna; McDonald, Clement J (2018) Response to Unit conversions between LOINC codes. J Am Med Inform Assoc 25:614-615 |
| Baik, Seo Hyon; Kury, Fabricio Sampaio Peres; McDonald, Clement Joseph (2017) Risk of Alzheimer's Disease Among Senior Medicare Beneficiaries Treated With Androgen Deprivation Therapy for Prostate Cancer. J Clin Oncol 35:3401-3409 |
| Mundkur, Mallika L; Callaghan, Fiona M; Abhyankar, Swapna et al. (2016) Use of Electronic Health Record Data to Evaluate the Impact of Race on 30-Day Mortality in Patients Admitted to the Intensive Care Unit. J Racial Ethn Health Disparities : |
| Deckard, Jamalynne; McDonald, Clement J; Vreeman, Daniel J (2015) Supporting interoperability of genetic data with LOINC. J Am Med Inform Assoc 22:621-7 |
| Zhu, Xinxin; Cimino, James J (2015) Clinicians' evaluation of computer-assisted medication summarization of electronic medical records. Comput Biol Med 59:221-31 |
| Drawz, Paul E; Archdeacon, Patrick; McDonald, Clement J et al. (2015) CKD as a Model for Improving Chronic Disease Care through Electronic Health Records. Clin J Am Soc Nephrol 10:1488-99 |
| Demner-Fushman, Dina; Kohli, Marc D; Rosenman, Marc B et al. (2015) Preparing a collection of radiology examinations for distribution and retrieval. J Am Med Inform Assoc : |
| Fung, Kin Wah; Xu, Julia (2015) An exploration of the properties of the CORE problem list subset and how it facilitates the implementation of SNOMED CT. J Am Med Inform Assoc 22:649-58 |
| Peters, Lee B; Bahr, Nathan; Bodenreider, Olivier (2015) Evaluating drug-drug interaction information in NDF-RT and DrugBank. J Biomed Semantics 6:19 |
| Fontelo, Paul; Liu, Fang; Yagi, Yukako (2015) Evaluation of a smartphone for telepathology: Lessons learned. J Pathol Inform 6:35 |
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