BackgroundHealth information technology (HIT) systems have the potential to reduce delayed, missed or incorrect diagnoses. We describe and classify the current state of diagnostic HIT and identify future research directions.Methods A multi-pronged literature search was conducted using PubMed, Web of Science, backwards and forwards reference searches and contributions from domain experts. We included HIT systems evaluated in clinical and experimental settings as well as previous reviews, and excluded radiology computer-aided diagnosis, monitor alerts and alarms, and studies focused on disease staging and prognosis. Articles were organised within a conceptual framework of the diagnostic process and areas requiring further investigation were identified.ResultsHIT approaches, tools and algorithms were identified and organised into 10 categories related to those assisting: (1) information gathering; (2) information organisation and display; (3) differential diagnosis generation; (4) weighing of diagnoses; (5) generation of diagnostic plan; (6) access to diagnostic reference information; (7) facilitating follow-up; (8) screening for early detection in asymptomatic patients; (9) collaborative diagnosis; and (10) facilitating diagnostic feedback to clinicians. We found many studies characterising potential interventions, but relatively few evaluating the interventions in actual clinical settings and even fewer demonstrating clinical impact.ConclusionsDiagnostic HIT research is still in its early stages with few demonstrations of measurable clinical impact. Future efforts need to focus on: (1) improving methods and criteria for measurement of the diagnostic process using electronic data; (2) better usability and interfaces in electronic health records; (3) more meaningful incorporation of evidence-based diagnostic protocols within clinical workflows; and (4) systematic feedback of diagnostic performance.

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The rate of appropriate drug prescribing in kidney impairment is low and remains a patient safety concern. Our results suggest that CDS improves drug prescribing, particularly when providing guidance on new prescriptions.

BackgroundPediatric providers are key players in the treatment of childhood obesity, yet rates of obesity management in the primary care setting are low. The goal of this study was to examine the views of pediatric providers on conducting obesity management in the primary care setting, and identify potential resources and care models that could facilitate delivery of this care.MethodsA mixed methods approach was utilized. Four focus groups were conducted with providers from a large pediatric network in San Diego County. Based on a priori and emerging themes, a questionnaire was developed and administered to the larger group of providers in this network.ResultsBarriers to conducting obesity management fell into four categories: provider-level/individual (e.g., lack of knowledge and confidence), practice-based/systems-level (e.g., lack of time and resources), parent-level (e.g., poor motivation and follow-up), and environmental (e.g., lack of access to resources). Solutions centered around implementing a team approach to care (with case managers and health coaches) and electronic medical record changes to include best practice guidelines, increased ease of documentation, and delivery of standardized handouts/resources. Survey results revealed only 23.8% of providers wanted to conduct behavioral management of obesity. The most requested support was the introduction of a health educator in the office to deliver a brief behavioral intervention.ConclusionWhile providers recognize the importance of addressing weight during a well-child visit, they do not want to conduct obesity management on their own. Future efforts to improve health outcomes for pediatric obesity should consider implementing a collaborative care approach.

BACKGROUND: Failure to follow up microbiology results pending at the time of hospital discharge can delay diagnosis and treatment of important infections, harm patients, and increase the risk of litigation. Current systems to track pending tests are often inadequate. OBJECTIVE: To design, implement, and evaluate an automated system to improve follow-up of microbiology results that return after hospitalized patients are discharged. DESIGN: Cluster randomized controlled trial. SUBJECTS: Inpatient and outpatient physicians caring for adult patients hospitalized at a large academic hospital from February 2009 to June 2010 with positive and untreated or undertreated blood, urine, sputum, or cerebral spinal fluid cultures returning post-discharge. INTERVENTION: An automated e-mail-based system alerting inpatient and outpatient physicians to positive post-discharge culture results not adequately treated with an antibiotic at the time of discharge. MAIN MEASURES: Our primary outcome was documented follow-up of results within 3 days. Secondary outcomes included physician awareness and assessment of result urgency, impact on clinical assessments and plans, and preferred alerting scenarios. KEY RESULTS: We evaluated the follow-up of 157 postdischarge microbiology results from patients of 121 physicians. We found documented follow-up in 27/97 (28%) results in the intervention group and 8/60 (13%) in the control group [aOR 3.2, (95% CI 1.3-8.4); p=0.01]. Of all inpatient physician respondents, 32/82 (39%) were previously aware of the results, 45/77 (58%) felt the results changed their assessments and plans, 43/77 (56%) felt the results required urgent action, and 67/70 (96%) preferred alerts for current or broader scenarios.CONCLUSION: Our alerting system improved the proportion of important post-discharge microbiology results with documented follow-up, though the proportion remained low. The alerts were well received and may be expanded in the future.KEY WORDS: reminder systems; pending test results; transitions of care; test result management; delays in diagnosis.

Adoption of Electronic Health Record (EHR) systems has led to collection of massive healthcare data, which creates oppor- tunities and challenges to study them. Computational phenotyping offers a promising way to convert the sparse and complex data into meaningful concepts that are interpretable to healthcare givers to make use of them. We propose a novel su- pervised nonnegative tensor factorization methodology that derives discriminative and distinct phenotypes. We represented co-occurrence of diagnoses and prescriptions in EHRs as a third-order tensor, and decomposed it using the CP algorithm. We evaluated discriminative power of our models with an Intensive Care Unit database (MIMIC-III) and demonstrated superior performance than state-of-the-art ICU mortality calculators (e.g., APACHE II, SAPS II). Example of the resulted phenotypes are sepsis with acute kidney injury, cardiac surgery, anemia, respiratory failure, heart failure, cardiac arrest, metastatic cancer (requiring ICU), end-stage dementia (requiring ICU and transitioned to comfort-care), intraabdominal conditions, and alcohol abuse/withdrawal.

Background: Learning patient outcomes is recognized as crucial for ongoing refinement of clinical decision-making, but is often difficult in fragmented care with frequent handoffs. Data on resident habits of seeking outcome feedback after handoffs is lacking. Methods: We performed a mixed-methods study including 1) an analysis of chart re-access rates after handoffs performed using access logs of the electronic health record; and 2) a web-based survey sent to internal medicine and emergency medicine residents about their habits of and barriers to learning the outcomes of patients after they have handed them off to other teams. Results: Residents on ward rotations were often able to re-access charts of patients after handoffs, but those on emergency medicine or night admitting rotations did so <5% of the time.Among residents surveyed, only a minority stated that they frequently find out the outcomes of patients they have handed off, although learning outcomes was important to both their education and job satisfaction. Most were not satisfied with current systems of learning outcomes of patients after handoffs, citing too little time and lack of reliable patient tracking systems as the main barriers. Conclusion: Despite perceived importance of learning outcomes after handoffs, residents cite difficulty with obtaining such information. Systematically providing feedback on patient outcomes would meet a recognized need among physicians in training.

The predictive value of VTE events flagged POA=N/U for HA-VTE was 75%. However, sole reliance on this flag may substantially underestimate the incidence of HA-VTE.