Wellesley, MA, United States
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PubMed | Partners HealthCare System Inc., MCPHS University, Catholic University of Louvain, Harvard University and 2 more.
Type: Journal Article | Journal: Journal of the American Medical Informatics Association : JAMIA | Year: 2016

Experts suggest that formulary alerts at the time of medication order entry are the most effective form of clinical decision support to automate formulary management.Our objectives were to quantify the frequency of inappropriate nonformulary medication (NFM) alert overrides in the inpatient setting and provide insight on how the design of formulary alerts could be improved.Alert overrides of the top 11 (n=206) most-utilized and highest-costing NFMs, from January 1 to December 31, 2012, were randomly selected for appropriateness evaluation. Using an empirically developed appropriateness algorithm, appropriateness of NFM alert overrides was assessed by 2 pharmacists via chart review. Appropriateness agreement of overrides was assessed with a Cohens kappa. We also assessed which types of NFMs were most likely to be inappropriately overridden, the override reasons that were disproportionately provided in the inappropriate overrides, and the specific reasons the overrides were considered inappropriate.Approximately 17.2% (n=35.4/206) of NFM alerts were inappropriately overridden. Non-oral NFM alerts were more likely to be inappropriately overridden compared to orals. Alerts overridden with blank reasons were more likely to be inappropriate. The failure to first try a formulary alternative was the most common reason for alerts being overridden inappropriately.Approximately 1 in 5 NFM alert overrides are overridden inappropriately. Future research should evaluate the impact of mandating a valid override reason and adding a list of formulary alternatives to each NFM alert; we speculate these NFM alert features may decrease the frequency of inappropriate overrides.


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 5 more authors.
Journal of the American Medical Informatics Association | Year: 2011

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.


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 | Year: 2010

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.


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 | Year: 2013

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.


Klann J.G.,Massachusetts General Hospital | Klann J.G.,Partners Healthcare System Inc. | Klann J.G.,Harvard University | Murphy S.N.,Massachusetts General Hospital | And 2 more authors.
Journal of Medical Internet Research | Year: 2013

Background: The Health Quality Measures Format (HQMF) is a Health Level 7 (HL7) standard for expressing computable Clinical Quality Measures (CQMs). Creating tools to process HQMF queries in clinical databases will become increasingly important as the United States moves forward with its Health Information Technology Strategic Plan to Stages 2 and 3 of the Meaningful Use incentive program (MU2 and MU3). Informatics for Integrating Biology and the Bedside (i2b2) is one of the analytical databases used as part of the Office of the National Coordinator (ONC)'s Query Health platform to move toward this goal. Objective: Our goal is to integrate i2b2 with the Query Health HQMF architecture, to prepare for other HQMF use-cases (such as MU2 and MU3), and to articulate the functional overlap between i2b2 and HQMF. Therefore, we analyze the structure of HQMF, and then we apply this understanding to HQMF computation on the i2b2 clinical analytical database platform. Specifically, we develop a translator between two query languages, HQMF and i2b2, so that the i2b2 platform can compute HQMF queries. Methods: We use the HQMF structure of queries for aggregate reporting, which define clinical data elements and the temporal and logical relationships between them. We use the i2b2 XML format, which allows flexible querying of a complex clinical data repository in an easy-to-understand domain-specific language. Results: The translator can represent nearly any i2b2-XML query as HQMF and execute in i2b2 nearly any HQMF query expressible in i2b2-XML. This translator is part of the freely available reference implementation of the QueryHealth initiative. We analyze limitations of the conversion and find it covers many, but not all, of the complex temporal and logical operators required by quality measures. Conclusions: HQMF is an expressive language for defining quality measures, and it will be important to understand and implement for CQM computation, in both meaningful use and population health. However, its current form might allow complexity that is intractable for current database systems (both in terms of implementation and computation). Our translator, which supports the subset of HQMF currently expressible in i2b2-XML, may represent the beginnings of a practical compromise. It is being pilot-tested in two Query Health demonstration projects, and it can be further expanded to balance computational tractability with the advanced features needed by measure developers.


Phansalkar S.,Brigham and Women's Hospital | Phansalkar S.,Partners Healthcare System Inc. | Phansalkar S.,Harvard University | Edworthy J.,University of Plymouth | And 7 more authors.
Journal of the American Medical Informatics Association | Year: 2010

The objective of this review is to describe the implementation of human factors principles for the design of alerts in clinical information systems. First, we conduct a review of alarm systems to identify human factors principles that are employed in the design and implementation of alerts. Second, we review the medical informatics literature to provide examples of the implementation of human factors principles in current clinical information systems using alerts to provide medication decision support. Last, we suggest actionable recommendations for delivering effective clinical decision support using alerts. A review of studies from the medical informatics literature suggests that many basic human factors principles are not followed, possibly contributing to the lack of acceptance of alerts in clinical information systems. We evaluate the limitations of current alerting philosophies and provide recommendations for improving acceptance of alerts by incorporating human factors principles in their design.


Fleurant M.,Boston Medical Center | Kell R.,Massachusetts eHealth Collaborative | Jenter C.,Brigham and Women's Hospital | Volk L.A.,Partners HealthCare System Inc. | And 7 more authors.
Journal of the American Medical Informatics Association | Year: 2012

Little is known about physicians' perception of the ease or difficulty of implementing electronic health records (EHR). This study identified factors related to the perceived difficulty of implementing EHR. 163 physicians completed surveys before and after the implementation of EHR in an externally funded pilot program in three Massachusetts communities. Ordinal hierarchical logistic regression was used to identify baseline factors that correlated with physicians' report of difficulty with EHR implementation. Compared with physicians with ownership stake in their practices, physician employees were less likely to describe EHR implementation as difficult (adjusted OR 0.5, 95% CI 0.3 to 1.0). Physicians who perceived their staff to be innovative were also less likely to view EHR implementation as difficult (adjusted OR 0.4, 95% CI 0.2 to 0.8). Physicians who own their practice may need more external support for EHR implementation than those who do not. Innovative clinical support staff may ease the EHR implementation process and contribute to its success.


Zhou L.,Partners HealthCare System Inc.
AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium | Year: 2011

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.


Shu T.,National Institute of Hospital Administration | Liu H.,Qinghua Changgeng Hospital | Goss F.R.,Tufts Medical Center | Yang W.,National Institute of Hospital Administration | And 5 more authors.
International Journal of Medical Informatics | Year: 2014

Heading: EHR adoption across China's tertiary hospitals: a cross-sectional observation study. Objectives: To assess electronic health record (EHR) adoption in Chinese tertiary hospitals using a nation-wide standard EHR grading model. Methods: The Model of EHR Grading (MEG) was used to assess the level of EHR adoption across 848 tertiary hospitals. MEG defines 37 EHR functions (e.g., order entry) which are grouped by 9 roles (e.g., inpatient physicians) and grades each function and the overall EHR adoption into eight levels (0-7). We assessed the MEG level of the involved hospitals and calculated the average score of the 37 EHR functions. A multivariate analysis was performed to explore the influencing factors (including hospital characteristics and information technology (IT) investment) of total score and scores of 9 roles. Results: Of the 848 hospitals, 260 (30.7%) were Level Zero, 102 (12.0%) were Level One, 269 (31.7%) were Level Two, 188 (22.2%) were Level Three, 23 (2.7%) were Level Four, 5 (0.6%) was Level Five, 1 (0.1%) were Level Six, and none achieved Level Seven. The scores of hospitals in eastern and western China were higher than those of hospitals in central areas. Bed size, outpatient admission, total income in 2011, percent of IT investment per income in 2011, IT investment in last 3 years, number of IT staff, and duration of EHR use were significant factors for total score. Conclusions: We examined levels of EHR adoption in 848 Chinese hospitals and found that most of them have only basic systems, around level 2 and 0. Very few have a higher score and level for clinical information using and sharing. © 2013 Elsevier Ireland Ltd.


Turchin A.,Partners HealthCare System Inc.
AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium | Year: 2011

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.

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