Last data update: May 06, 2024. (Total: 46732 publications since 2009)
Records 1-3 (of 3 Records) |
Query Trace: Mishra NK[original query] |
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Public health reporting and outbreak response: synergies with evolving clinical standards for interoperability
Mishra NK , Duke J , Lenert L , Karki S . J Am Med Inform Assoc 2020 27 (7) 1136-1138 Public health needs up-to-date information for surveillance and response. As healthcare application programming interfaces become widely available, a novel data gathering mechanism could provide public health with critical information in a timely fashion to respond to a fast-moving epidemic. In this article, we extrapolate from our experiences using a Fast Healthcare Interoperability Resource-based architecture for infectious disease surveillance for sexually transmitted diseases to its application to gather case information for an outbreak. One of the challenges with a fast-moving outbreak is to accurately assess its demand on healthcare resources, since information specific to comorbidities is often not available. These comorbidities are often associated with poor prognosis and higher resource utilization. If the comorbidity data and other clinical information were readily available to public health workers, they could better address community disruption and manage healthcare resources. The use of FHIR resources available through application programming and filtered through tools such as described herein will give public health the flexibility needed to investigate rapidly emerging disease while protecting patient privacy. |
Leveraging informatics to identify reportable cases: Pilot findings on electronic case reporting of chlamydia and gonorrhea
Mishra NK , Jellison JB , Hamilton A , Carr JB , Padilla RM , Viator NA . J Public Health Manag Pract 2019 25 (6) 595-597 Consensus-based technical guidance for electronic case reporting (eCR) of sexually transmitted infections was implemented within existing health information technologies to automatically detect chlamydia and gonorrhea cases based on diagnosis and laboratory observation codes and build a case report using industry standards. The process was evaluated using 12 420 ambulatory encounters among adolescents and adults 15 years and older seen at 8 Chicago-area community health centers between May 1 and June 30, 2017. We tabulated the frequency of matches between the case detection logic and patient data and compared the eCR identified cases with paper case reports. This study found that eCR increased provider reporting when compared with paper reporting alone. While additional work across stakeholder groups is needed, these early findings suggest that broadly adopted eCR will decrease both provider and public health burden while improving reporting timeliness and data completion to support case investigation. |
Towards automatic diabetes case detection and ABCS protocol compliance assessment
Mishra NK , Son RY , Arnzen JJ . Clin Med Res 2012 10 (3) 106-21 OBJECTIVE: According to the American Diabetes Association, the implementation of the standards of care for diabetes has been suboptimal in most clinical settings. Diabetes is a disease that had a total estimated cost of $174 billion in 2007 for an estimated diabetes-affected population of 17.5 million in the United States. With the advent of electronic medical records (EMR), tools to analyze data residing in the EMR for healthcare surveillance can help reduce the burdens experienced today. This study was primarily designed to evaluate the efficacy of employing clinical natural language processing to analyze discharge summaries for evidence indicating a presence of diabetes, as well as to assess diabetes protocol compliance and high risk factors. METHODS: Three sets of algorithms were developed to analyze discharge summaries for: (1) identification of diabetes, (2) protocol compliance, and (3) identification of high risk factors. The algorithms utilize a common natural language processing framework that extracts relevant discourse evidence from the medical text. Evidence utilized in one or more of the algorithms include assertion of the disease and associated findings in medical text, as well as numerical clinical measurements and prescribed medications. RESULTS: The diabetes classifier was successful at classifying reports for the presence and absence of diabetes. Evaluated against 444 discharge summaries, the classifier's performance included macro and micro F-scores of 0.9698 and 0.9865, respectively. Furthermore, the protocol compliance and high risk factor classifiers showed promising results, with most F-measures exceeding 0.9. CONCLUSIONS: The presented approach accurately identified diabetes in medical discharge summaries and showed promise with regards to assessment of protocol compliance and high risk factors. Utilizing free-text analytic techniques on medical text can complement clinical-public health decision support by identifying cases and high risk factors. |
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