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) has embarked on the development and deployment of a PHR in order to study and improve PHRs'utility, reduce the barriers to their use, identify best practices, and provide a platform and test bed for advanced applications. The development is based on a set of existing messages and vocabulary standards that are supported by the Department of Health and Human Services and the NLM, and on both existing (Ruby on Rails, Scriptaculous, and jQuery) and NLM-developed open source software building blocks, some of which will have broad medical informatics applications. The PHR is a web application based on an internally developed forms generator with rule-based skip logic (e.g. if the person is male, it does not ask about pregnancy history), edit checks and auto complete input. It uses AJAX techniques, 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 (vaccines), and important measurements such as blood pressure and laboratory results (e.g. serum glucose). The PHR assigns standard terminology codes to the major clinical variables - e.g. drugs, problems, surgeries, immunizations - that it carries. The coding maps the terms that consumers enter to federally-supported coding systems, e.g. drugs are mapped to RxTerms (a subset of RxNorm that was developed for this project and adopted by The Centers for Medicare and Medicaid Services (CMS) for their post-acute care project), measurements are mapped to Logical Observation Identifiers Names and Codes (LOINC), immunizations are mapped to the Centers for Disease Control's (CDC) vaccine CVX codes, and diagnoses and procedures are mapped to Systematized Nomenclature of MedicineClinical Terms (SNOMED-CT). The PHR adopts Health Level 7 (HL7) standards for much of its data structure and all of its data types. These capabilities facilitate importing clinical data from widely available HL7 messages. Automatic coding in the NLM PHR provides three special capabilities: 1) custom clinical decision support, 2) one-click access to information about coded concepts entered in the PHR, and 3) identification of duplicate prescribing with different but equivalent drugs. During FY2011, the following enhancements were made or begun: o Began full design of look and feel of the web site with the help of a graphics designer (in progress). o Revised the medical conditions list and expanded the surgeries list. o Added prescription frequency data from United Healthcare to assure that the most frequently-used medications appear in the auto-completion pick-list and thus to speed the users selection. o Added sophisticated graphing (including a sparkline graph at the beginning of each row of the flowsheet);so users can now see graphically how parameters such as blood pressure have changed over time and how they compare with normal ranges. o Added the ability to graph one or more variables in a single display and zoom in to see details. o Added an auto-save feature that enables recovery of unsaved data if the computer or browser crashes. o Various performance improvements and security enhancements, including a revision of the password and account ID recovery system. o Revised the term class management system (and its web pages) to support hierarchies of classes. o Created several new consumer health panels, including sleep and nutrition trackers. o Added new custom reminder rules regarding preventive health as well as interventions for specific health conditions. This is a young project to produce something that 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. It also will provide another consumer entry point to NLM's rich trove of patient-oriented data. Early research projects will be focused on users'needs, usability, and usage patterns to guide the next round of development and research. Subject to review by senior administration, Bethesdas Suburban Hospital has tentatively agreed to install and operate the NLM PHR as a service to people in the community, and to provide de-identified data to NLM so NLM can research usability and refine the software.
| 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 |
Showing the most recent 10 out of 31 publications
