Joshua C Feblowitz

Publications of Joshua C Feblowitz

• Improving completeness of electronic problem lists through clinical decision support: a randomized, controlled trial.

  Authors: Adam Wright, Justine Pang, Joshua C Feblowitz, Francine L Maloney, Allison R Wilcox, Karen Sax McLoughlin, Harley Ramelson, Louise Schneider, David W Bates

  Journal of the American Medical Informatics Association : JAMIA. 01/2012;

  BackgroundAccurate clinical problem lists are critical for patient care, clinical decision support, population reporting, quality improvement, and research. However, problem lists are oftenBackgroundAccurate clinical problem lists are critical for patient care, clinical decision support, population reporting, quality improvement, and research. However, problem lists are often incomplete or out of date.ObjectiveTo determine whether a clinical alerting system, which uses inference rules to notify providers of undocumented problems, improves problem list documentation.Study Design and MethodsInference rules for 17 conditions were constructed and an electronic health record-based intervention was evaluated to improve problem documentation. A cluster randomized trial was conducted of 11 participating clinics affiliated with a large academic medical center, totaling 28 primary care clinical areas, with 14 receiving the intervention and 14 as controls. The intervention was a clinical alert directed to the provider that suggested adding a problem to the electronic problem list based on inference rules. The primary outcome measure was acceptance of the alert. The number of study problems added in each arm as a pre-specified secondary outcome was also assessed. Data were collected during 6-month pre-intervention (11/2009-5/2010) and intervention (5/2010-11/2010) periods.Results17 043 alerts were presented, of which 41.1% were accepted. In the intervention arm, providers documented significantly more study problems (adjusted OR=3.4, p<0.001), with an absolute difference of 6277 additional problems. In the intervention group, 70.4% of all study problems were added via the problem list alerts. Significant increases in problem notation were observed for 13 of 17 conditions.ConclusionProblem inference alerts significantly increase notation of important patient problems in primary care, which in turn has the potential to facilitate quality improvement.Trial RegistrationClinicalTrials.gov: NCT01105923.

• A method and knowledge base for automated inference of patient problems from structured data in an electronic medical record.

  Authors: Adam Wright, Justine Pang, Joshua C Feblowitz, Francine L Maloney, Allison R Wilcox, Harley Z Ramelson, Louise I Schneider, David W Bates

  Journal of the American Medical Informatics Association : JAMIA. 05/2011; 18(6):859-67.

  Accurate knowledge of a patient's medical problems is critical for clinical decision making, quality measurement, research, billing and clinical decision support. Common structured sources of problemAccurate knowledge of a patient's medical problems is critical for clinical decision making, quality measurement, research, billing and clinical decision support. Common structured sources of problem information include the patient problem list and billing data; however, these sources are often inaccurate or incomplete. To develop and validate methods of automatically inferring patient problems from clinical and billing data, and to provide a knowledge base for inferring problems. We identified 17 target conditions and designed and validated a set of rules for identifying patient problems based on medications, laboratory results, billing codes, and vital signs. A panel of physicians provided input on a preliminary set of rules. Based on this input, we tested candidate rules on a sample of 100,000 patient records to assess their performance compared to gold standard manual chart review. The physician panel selected a final rule for each condition, which was validated on an independent sample of 100,000 records to assess its accuracy. Seventeen rules were developed for inferring patient problems. Analysis using a validation set of 100,000 randomly selected patients showed high sensitivity (range: 62.8-100.0%) and positive predictive value (range: 79.8-99.6%) for most rules. Overall, the inference rules performed better than using either the problem list or billing data alone. We developed and validated a set of rules for inferring patient problems. These rules have a variety of applications, including clinical decision support, care improvement, augmentation of the problem list, and identification of patients for research cohorts.

• Summarization of clinical information: a conceptual model.

  Authors: Joshua C Feblowitz, Adam Wright, Hardeep Singh, Lipika Samal, Dean F Sittig

  Journal of biomedical informatics. 03/2011; 44(4):688-99.

  To provide high-quality and safe care, clinicians must be able to optimally collect, distill, and interpret patient information. Despite advances in text summarization, only limited research existsTo provide high-quality and safe care, clinicians must be able to optimally collect, distill, and interpret patient information. Despite advances in text summarization, only limited research exists on clinical summarization, the complex and heterogeneous process of gathering, organizing and presenting patient data in various forms. To develop a conceptual model for describing and understanding clinical summarization in both computer-independent and computer-supported clinical tasks. Based on extensive literature review and clinical input, we developed a conceptual model of clinical summarization to lay the foundation for future research on clinician workflow and automated summarization using electronic health records (EHRs). Our model identifies five distinct stages of clinical summarization: (1) Aggregation, (2) Organization, (3) Reduction and/or Transformation, (4) Interpretation and (5) Synthesis (AORTIS). The AORTIS model describes the creation of complex, task-specific clinical summaries and provides a framework for clinical workflow analysis and directed research on test results review, clinical documentation and medical decision-making. We describe a hypothetical case study to illustrate the application of this model in the primary care setting. Both practicing physicians and clinical informaticians need a structured method of developing, studying and evaluating clinical summaries in support of a wide range of clinical tasks. Our proposed model of clinical summarization provides a potential pathway to advance knowledge in this area and highlights directions for further research.

• Clinician attitudes toward and use of electronic problem lists: a thematic analysis.

  Authors: Adam Wright, Francine L Maloney, Joshua C Feblowitz

  BMC medical informatics and decision making. 01/2011; 11:36.

  The clinical problem list is an important tool for clinical decision making, quality measurement and clinical decision support; however, problem lists are often incomplete and provider attitudesThe clinical problem list is an important tool for clinical decision making, quality measurement and clinical decision support; however, problem lists are often incomplete and provider attitudes towards the problem list are poorly understood. An ethnographic study of healthcare providers conducted from April 2009 to January 2010 was carried out among academic and community outpatient medical practices in the Greater Boston area across a wide range of medical and surgical specialties. Attitudes towards the problem list were then analyzed using grounded theory methods. Attitudes were variable, and dimensions of variations fit into nine themes: workflow, ownership and responsibility, relevance, uses, content, presentation, accuracy, alternatives, support/education and one cross-cutting theme of culture. Significant variation was observed in clinician attitudes towards and use of the electronic patient problem list. Clearer guidance and best practices for problem list utilization are needed.