Chiang Jao

Publications (31) View all

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  Available from: Chiang Jao

  Article: Computerized physician order entry of medications and clinical decision support can improve problem list documentation compliance.

  William L Galanter, Daniel B Hier, Chiang Jao, David Sarne
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  ABSTRACT: The problem list is a key and required element of the electronic medical record (EMR). Problem lists may contribute substantially to patient safety and quality of care. Physician documentation of the problem list is often lower than desired. Methods are needed to improve accuracy and completeness of the problem list. An automated clinical decision support (CDS) intervention was designed utilizing a commercially available EMR with computerized physician order entry (CPOE) and CDS. The system was based on alerts delivered during inpatient medication CPOE that prompted clinicians to add a diagnosis to the problem list. Each alert was studied for a 2-month period after implementation. Measures included alert validity, alert yield, and accuracy of problem list additions. At a 450 bed teaching hospital, the number of medication orders which triggered alerts during all 2-month study periods was 1011. For all the alerts, the likelihood of a valid alert (an alert that occurred in patients with one of the predefined diagnoses) was 96+/-1%. The alert yield, defined as occuring when an alert led to addition of a problem to the problem list, was 76+/-2%. Accurate problem list additions, defined as additions of problems when the problem was determined to be present by expert review, was 95+/-1%. The CDS problem list mechanism was integrated into the process of medication order placement and promoted relatively accurate addition of problems to the EMR problem list.
  International Journal of Medical Informatics 08/2008; 79(5):332-8. · 2.41 Impact Factor
• Article: Automating the maintenance of problem list documentation using a clinical decision support system.

  Chiang Jao, Daniel Hier, William Galanter
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  ABSTRACT: We designed and tested a clinical decision support system (CDSS) prototype to investigate whether a CDSS that assists matching ordered drugs to problems on the problem list can enhance the maintenance of medications and problem lists in the electronic medical record. We evaluated the capability of this CDSS in medication-problem matching using clinical expert chart audits. The analysis revealed that this CDSS could determine the completeness of medication and problem lists if a mismatch occurs.
  AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium 02/2008;
• Article: Assessing physician comprehension of and attitudes toward problem list documentation.

  Chiang Jao, Daniel Hier, William Galanter, Annette Valenta
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  ABSTRACT: We conducted an online survey to assess the knowledge, attitudes, and practice patterns of physicians related to issues in problem list documentation. Respondents felt that a decision support tool to improve problem list documentation would benefit patient safety more than physician productivity. The majority of respondents are reluctant to maintain medication and problem lists and the quality of documentation remains inadequate.
  AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium 02/2008;
• Article: Porting a handheld cognitive assessment form to a mental expert system

  Chiang Jao, Winifred Dollar, Jian Su
  Neuroinformatics 05/2003; 1(2):203-205. · 2.97 Impact Factor
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  Available from: Chiang Jao

  Article: Extracting drug indication information from structured product labels using natural language processing.

  Kin Wah Fung, Chiang S Jao, Dina Demner-Fushman
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  ABSTRACT: OBJECTIVE: To extract drug indications from structured drug labels and represent the information using codes from standard medical terminologies. MATERIALS AND METHODS: We used MetaMap and other publicly available resources to extract information from the indications section of drug labels. Drugs and indications were encoded by RxNorm and UMLS identifiers respectively. A sample was manually reviewed. We also compared the results with two independent information sources: National Drug File-Reference Terminology and the Semantic Medline project. RESULTS: A total of 6797 drug labels were processed, resulting in 19 473 unique drug-indication pairs. Manual review of 298 most frequently prescribed drugs by seven physicians showed a recall of 0.95 and precision of 0.77. Inter-rater agreement (Fleiss κ) was 0.713. The precision of the subset of results corroborated by Semantic Medline extractions increased to 0.93. DISCUSSION: Correlation of a patient's medical problems and drugs in an electronic health record has been used to improve data quality and reduce medication errors. Authoritative drug indication information is available from drug labels, but not in a format readily usable by computer applications. Our study shows that it is feasible to use publicly available natural language processing resources to extract drug indications from drug labels. The same method can be applied to other sections of the drug label-for example, adverse effects, contraindications. CONCLUSIONS: It is feasible to use publicly available natural language processing tools to extract indication information from freely available drug labels. Named entity recognition sources (eg, MetaMap) provide reasonable recall. Combination with other data sources provides higher precision.
  Journal of the American Medical Informatics Association 03/2013; · 3.61 Impact Factor