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Wellesley, MA, United States

Zhou L.,Partners HealthCare System Inc.
AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium

Clinical information is often coded using different terminologies, and therefore is not interoperable. Our goal is to develop a general natural language processing (NLP) system, called Medical Text Extraction, Reasoning and Mapping System (MTERMS), which encodes clinical text using different terminologies and simultaneously establishes dynamic mappings between them. MTERMS applies a modular, pipeline approach flowing from a preprocessor, semantic tagger, terminology mapper, context analyzer, and parser to structure inputted clinical notes. Evaluators manually reviewed 30 free-text and 10 structured outpatient clinical notes compared to MTERMS output. MTERMS achieved an overall F-measure of 90.6 and 94.0 for free-text and structured notes respectively for medication and temporal information. The local medication terminology had 83.0% coverage compared to RxNorm's 98.0% coverage for free-text notes. 61.6% of mappings between the terminologies are exact match. Capture of duration was significantly improved (91.7% vs. 52.5%) from systems in the third i2b2 challenge. Source

Klann J.G.,Massachusetts General Hospital | Klann J.G.,Harvard University | Klann J.G.,Partners HealthCare System Inc. | Anand V.,Indiana University | And 3 more authors.
Journal of the American Medical Informatics Association

Objective Over 8 years, we have developed an innovative computer decision support system that improves appropriate delivery of pediatric screening and care. This system employs a guidelines evaluation engine using data from the electronic health record (EHR) and input from patients and caregivers. Because guideline recommendations typically exceed the scope of one visit, the engine uses a static prioritization scheme to select recommendations. Here we extend an earlier idea to create patient-tailored prioritization. Materials and methods We used Bayesian structure learning to build networks of association among previously collected data from our decision support system. Using area under the receiver-operating characteristic curve (AUC) as a measure of discriminability (a sine qua non for expected value calculations needed for prioritization), we performed a structural analysis of variables with high AUC on a test set. Our source data included 177 variables for 29 402 patients. Results The method produced a network model containing 78 screening questions and anticipatory guidance (107 variables total). Average AUC was 0.65, which is sufficient for prioritization depending on factors such as population prevalence. Structure analysis of seven highly predictive variables reveals both face-validity (related nodes are connected) and non-intuitive relationships. Discussion We demonstrate the ability of a Bayesian structure learning method to 'phenotype the population' seen in our primary care pediatric clinics. The resulting network can be used to produce patient-tailored posterior probabilities that can be used to prioritize content based on the patient's current circumstances. Conclusions This study demonstrates the feasibility of EHR-driven population phenotyping for patient-tailored prioritization of pediatric preventive care services. Source

Turchin A.,Partners HealthCare System Inc.
AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium

Electronic prescribing can reduce certain types of medication errors but can also facilitate new types of errors. Internal prescription discrepancies arise when information in the structured (dose, frequency) fields conflicts with instructions given in the free-text field on the prescription, and are unique to electronic prescribing. It is not known whether internal prescription discrepancies lead to adverse events.We have conducted a case-control study to determine whether internal discrepancies in warfarin prescriptions are associated with an increased risk of hemorrhage. We compared frequency of internal discrepancies in warfarin prescriptions between 573 patients admitted for a major hemorrhage and 1,719 controls. In multivariable analysis case patients had the odds of 0.61 of having an internal discrepancy in the most recent warfarin prescription (p = 0.045) compared to controls.Consequences of EMR errors may not be obvious. Studies that directly examine clinical outcomes are necessary to identify categories of EMR errors likely to cause patient harm. Source

Murphy S.N.,Massachusetts General Hospital | Murphy S.N.,Harvard University | Murphy S.N.,Partners HealthCare System Inc. | Gainer V.,Partners HealthCare System Inc. | And 4 more authors.
Journal of the American Medical Informatics Association

Background: The re-use of patient data from electronic healthcare record systems can provide tremendous benefits for clinical research, but measures to protect patient privacy while utilizing these records have many challenges. Some of these challenges arise from a misperception that the problem should be solved technically when actually the problem needs a holistic solution. Objective: The authors' experience with informatics for integrating biology and the bedside (i2b2) use cases indicates that the privacy of the patient should be considered on three fronts: technical de-identification of the data, trust in the researcher and the research, and the security of the underlying technical platforms. Methods: The security structure of i2b2 is implemented based on consideration of all three fronts. It has been supported with several use cases across the USA, resulting in five privacy categories of users that serve to protect the data while supporting the use cases. Results: The i2b2 architecture is designed to provide consistency and faithfully implement these user privacy categories. These privacy categories help reflect the policy of both the Health Insurance Portability and Accountability Act and the provisions of the National Research Act of 1974, as embodied by current institutional review boards. Conclusion: By implementing a holistic approach to patient privacy solutions, i2b2 is able to help close the gap between principle and practice. Source

Murphy S.N.,Massachusetts General Hospital | Murphy S.N.,Partners HealthCare System Inc. | Weber G.,Harvard University | Weber G.,Beth Israel Deaconess Medical Center | And 6 more authors.
Journal of the American Medical Informatics Association

Informatics for Integrating Biology and the Bedside (i2b2) is one of seven projects sponsored by the NIH Roadmap National Centers for Biomedical Computing (http://www.ncbcs.org). Its mission is to provide clinical investigators with the tools necessary to integrate medical record and clinical research data in the genomics age, a software suite to construct and integrate the modern clinical research chart. i2b2 software may be used by an enterprise's research community to find sets of interesting patients from electronic patient medical record data, while preserving patient privacy through a query tool interface. Project-specific mini-databases ("data marts") can be created from these sets to make highly detailed data available on these specific patients to the investigators on the i2b2 platform, as reviewed and restricted by the Institutional Review Board. The current version of this software has been released into the public domain and is available at the URL: http://www.i2b2.org/software. Source

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