Joshua Feblowitz

Early Results of the Meaningful Use Program for Electronic Health Records

The HITECH Act created incentives to encourage adoption of electronic health records. As of May 2012, only 12.2% of 62,226 eligible professionals had attested to meaningful use, including 9.8% of specialists and 17.8% of primary care providers.

Summarization of clinical information: A conceptual model

BACKGROUND 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 exists on clinical summarization, the complex and heterogeneous process of gathering, organizing and presenting patient data in various forms. OBJECTIVE To develop a conceptual model for describing and understanding clinical summarization in both computer-independent and computer-supported clinical tasks. DESIGN 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). RESULTS 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. CONCLUSION 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.

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

BACKGROUND Accurate knowledge of a patients 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. OBJECTIVE To develop and validate methods of automatically inferring patient problems from clinical and billing data, and to provide a knowledge base for inferring problems. STUDY DESIGN AND METHODS 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. RESULTS 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. CONCLUSION 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.

Development and evaluation of a comprehensive clinical decision support taxonomy: comparison of front-end tools in commercial and internally developed electronic health record systems

BACKGROUND Clinical decision support (CDS) is a valuable tool for improving healthcare quality and lowering costs. However, there is no comprehensive taxonomy of types of CDS and there has been limited research on the availability of various CDS tools across current electronic health record (EHR) systems. OBJECTIVE To develop and validate a taxonomy of front-end CDS tools and to assess support for these tools in major commercial and internally developed EHRs. STUDY DESIGN AND METHODS We used a modified Delphi approach with a panel of 11 decision support experts to develop a taxonomy of 53 front-end CDS tools. Based on this taxonomy, a survey on CDS tools was sent to a purposive sample of commercial EHR vendors (n=9) and leading healthcare institutions with internally developed state-of-the-art EHRs (n=4). RESULTS Responses were received from all healthcare institutions and 7 of 9 EHR vendors (response rate: 85%). All 53 types of CDS tools identified in the taxonomy were found in at least one surveyed EHR system, but only 8 functions were present in all EHRs. Medication dosing support and order facilitators were the most commonly available classes of decision support, while expert systems (eg, diagnostic decision support, ventilator management suggestions) were the least common. CONCLUSION We developed and validated a comprehensive taxonomy of front-end CDS tools. A subsequent survey of commercial EHR vendors and leading healthcare institutions revealed a small core set of common CDS tools, but identified significant variability in the remainder of clinical decision support content.

Randomized Controlled Trial of Health Maintenance Reminders Provided Directly to Patients Through an Electronic PHR

BACKGROUNDProvider and patient reminders can be effective in increasing rates of preventive screenings and vaccinations. However, the effect of patient-directed electronic reminders is understudied.OBJECTIVETo determine whether providing reminders directly to patients via an electronic Personal Health Record (PHR) improved adherence to care recommendations.DESIGNWe conducted a cluster randomized trial without blinding from 2005 to 2007 at 11 primary care practices in the Partners HealthCare system.PARTICIPANTSA total of 21,533 patients with access to a PHR were invited to the study, and 3,979 (18.5%) consented to enroll.INTERVENTIONSPatients in the intervention arm received health maintenance (HM) reminders via a secure PHR “eJournal,” which allowed them to review and update HM and family history information. Patients in the active control arm received access to an eJournal that allowed them to input and review information related to medications, allergies and diabetes management.MAIN MEASURESThe primary outcome measure was adherence to guideline-based care recommendations.KEY RESULTSIntention-to-treat analysis showed that patients in the intervention arm were significantly more likely to receive mammography (48.6% vs 29.5%, p = 0.006) and influenza vaccinations (22.0% vs 14.0%, p = 0.018). No significant improvement was observed in rates of other screenings. Although Pap smear completion rates were higher in the intervention arm (41.0% vs 10.4%, p < 0.001), this finding was no longer significant after excluding women’s health clinics. Additional on-treatment analysis showed significant increases in mammography (p = 0.019) and influenza vaccination (p = 0.015) for intervention arm patients who opened an eJournal compared to control arm patients, but no differences for any measure among patients who did not open an eJournal.CONCLUSIONSProviding patients with HM reminders via a PHR may be effective in improving some elements of preventive care.

Governance for clinical decision support: case studies and recommended practices from leading institutions

OBJECTIVE Clinical decision support (CDS) is a powerful tool for improving healthcare quality and ensuring patient safety; however, effective implementation of CDS requires effective clinical and technical governance structures. The authors sought to determine the range and variety of these governance structures and identify a set of recommended practices through observational study. DESIGN Three site visits were conducted at institutions across the USA to learn about CDS capabilities and processes from clinical, technical, and organizational perspectives. Based on the results of these visits, written questionnaires were sent to the three institutions visited and two additional sites. Together, these five organizations encompass a variety of academic and community hospitals as well as small and large ambulatory practices. These organizations use both commercially available and internally developed clinical information systems. MEASUREMENTS Characteristics of clinical information systems and CDS systems used at each site as well as governance structures and content management approaches were identified through extensive field interviews and follow-up surveys. RESULTS Six recommended practices were identified in the area of governance, and four were identified in the area of content management. Key similarities and differences between the organizations studied were also highlighted. CONCLUSION Each of the five sites studied contributed to the recommended practices presented in this paper for CDS governance. Since these strategies appear to be useful at a diverse range of institutions, they should be considered by any future implementers of decision support.

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

BackgroundThe 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.MethodsAn 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.ResultsAttitudes 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.ConclusionsSignificant 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.

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

Background Accurate 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. Objective To determine whether a clinical alerting system, which uses inference rules to notify providers of undocumented problems, improves problem list documentation. Study Design and Methods Inference 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. Results 17 043 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. Conclusion Problem inference alerts significantly increase notation of important patient problems in primary care, which in turn has the potential to facilitate quality improvement. Trial Registration ClinicalTrials.gov: NCT01105923.

The Medicare Electronic Health Record Incentive Program: Provider Performance on Core and Menu Measures

OBJECTIVE To measure performance by eligible health care providers on CMSs meaningful use measures. DATA SOURCE Medicare Electronic Health Record Incentive Program Eligible Professionals Public Use File (PUF), which contains data on meaningful use attestations by 237,267 eligible providers through May 31, 2013. STUDY DESIGN Cross-sectional analysis of the 15 core and 10 menu measures pertaining to use of EHR functions reported in the PUF. PRINCIPAL FINDINGS Providers in the dataset performed strongly on all core measures, with the most frequent response for each of the 15 measures being 90-100 percent compliance, even when the threshold for a particular measure was lower (e.g., 30 percent). PCPs had higher scores than specialists for computerized order entry, maintaining an active medication list, and documenting vital signs, while specialists had higher scores for maintaining a problem list, recording patient demographics and smoking status, and for providing patients with an after-visit summary. In fact, 90.2 percent of eligible providers claimed at least one exclusion, and half claimed two or more. CONCLUSIONS Providers are successfully attesting to CMSs requirements, and often exceeding the thresholds required by CMS; however, some troubling patterns in exclusions are present. CMS should raise program requirements in future years.

Preventability of adverse drug events involving multiple drugs using publicly available clinical decision support tools

PURPOSE The results of a retrospective evaluation of the frequency and preventability of adverse drug events (ADEs) involving multiple drugs among hospital inpatients are reported. METHODS Data collected in a previous cohort study of 180 actual ADEs and 552 potential ADEs (PADEs) at six community hospitals in Massachusetts were analyzed to determine the frequency and types of multiple-drug ADEs and the extent to which the ADEs might have been prevented using publicly available clinical decision-support (CDS) knowledge bases. None of the hospitals had a computerized prescriber-order-entry system at the time of data collection (January 2005-August 2006). RESULTS A total of 17 ADEs (rate, 1.4 per 100 admissions) and 146 PADEs (rate, 12.2 per 100 admissions) involving multiple drugs were identified. The documented events were related to drug duplication (n = 126), drug-drug interaction (n = 21), additive effects (n = 14), and therapeutic duplication (n = 7) or a combination of those factors. The majority of actual ADEs were due to drug-drug interactions, most commonly involving opioids, benzodiazepines, or cardiac medications; about 75% of the PADEs involved excessive drug doses resulting from order duplication or the prescribing of combination drugs with overlapping ingredients, usually products containing acetaminophen and an opioid. It was determined that 5 (29.4%) of the ADEs and 131 (89.7%) of the PADEs could have been detected through the use of the evaluated CDS tools. CONCLUSION A substantial number of actual ADEs and PADEs in the community hospital setting may be preventable through the use of publicly available CDS knowledge bases.