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 Personal health record (PHR) in order to study and improve their 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 Dojo) and NLM-developed open source software building blocks, some of which will have broad medical informatics applications. It 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 provides tools for managing clinical information from many different family members, so a mother can maintain immunization records for herself and each of her children, and/or keep track of her ailing father's medications. It provides places for recording medications, medical problems, surgeries, immunizations (vaccines), important measurements such as blood pressure and laboratory results (e.g. serum glucose). It is designed to encode the names of the major facts - e.g. drugs, problems, surgeries, immunizations - that it carries. The encoding translates names into federally-supported coding systems, e.g. 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) for drugs, Logical Observation Identifiers Names and Codes (LOINC) for measurements, the Centers for Disease Control's (CDC) vaccine codes for immunizations, Systematized Nomenclature of Medicine (SNOMED) for diagnoses. It adopts Health Level 7 (HL7) for much of its data structure and all of its data types. Both of these capabilities facilitate the importation of clinical data from widely available HL7 messages. The orientation toward automatic coding enables the NLM PHR to provide two special capabilities: 1) decision support rules and 2) one-click access to information about coded concepts recorded in the record. During FY2010 we reviewed all of the medical conditions, surgical interventions and vaccines- terms in the coding system, applied uniform styles to each, added synonyms and consumer names and expanded the number of concepts by 50%. We also created a form that enables clinicians to use an English-like Syntax to author and edit rules that can examine and react to patterns of patient data in the PHR. This rule can be used to hide or skip input fields on the main form, to generate default value for fields and/or to suggest preventive care interventions recommended by authoritative bodies such as the US Preventive Services Task Force, or the CDC. The following shows the exclusion rules and the first portion of the reminder text for two of the eight cases used to generate reminders for colon cancer screening. Colon_Cancer_Screening Exclusion Criteria: age <50 OR age >= 86 OR (Years_since_ last_colonoscopy <= 10) OR (Years_since_last_ flexsig <= ) OR Years_since_last _FOBT <=1 Rule NO FOBT AND NO Flexsig and No Colonoscopy and age <76 Reminder text The U.S. Preventive Services Task Force recommends routine colon cancer screening for people of Gender_possesive age www.ahrq.gov/clinic/uspstf/uspscolo.htm) because screening reduces death from colon cancer in this age group. ... Rule Years_since_colonoscopy >10 AND age <76 Reminder text According to demographic_infoPHR Record Name's PHR, Gender_possesive last colonoscopy was more than 10 years ago, on Latest_colonoscopyDate. The U.S. Preventive Services Task Force recommends regular colon cancer screening for people 50 to 75 years old ( see www.ahrq.gov/clinic/uspstf/uspscolo.htm) because screening reduces death from colon cancer in this age group. ... The first cell in each row contains a Rule and the second cell contains the Reminder text that will be delivered to the patient when the rule is satisfied. The table operates like a computer choice statement, and only the first row that has a rule that is satisfied will trigger a reminder. Note that each pair of curlicue brackets represents an item of patient-specific information that will be inserted into the text to tailor it to the patient. So Gender possessive will become his if the patient is a male and her if a female. URLS can be inserted in the reminder text and users can click on the URL to get to the monographs stored on the respective sites. We have written and tested rules for more than 15 preventive interventions including cancer screening and immunization and we are working on another 20. The PHR links all of the coded concepts, drugs, medical conditions, and allergies to sources of trusted information, including MedlinePlus, Genetics Home Reference, the CDC, and the US Preventive Health Service Task Force, and populates the decision support reminders and the consumer-tailored information. In the last year we also created all of the base tools needed for security, e.g., CAPTCHA, machinery for detecting when the user is using a new computer and thus is obliged to re-enter the answer to their secrete questions, for timing out after a number of failed log on attempts and for user recovery of their ID and passwords if forgotten. We also rebuilt the field entry and menu display algorithms to make them faster, simpler, and conformant with common web site protocols, and we added due date reminders and a flow sheet display capability. This is a young project to produce something that has been a longstanding NLM interest and is part of the NLM strategic plan. It will use and tune the message and vocabulary standards that NLM has supported and also 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 their community.
| 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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