Andrew Y. Shin

Use of a Checklist and Clinical Decision Support Tool Reduces Laboratory Use and Improves Cost.

OBJECTIVE: We hypothesized that a daily rounding checklist and a computerized order entry (CPOE) rule that limited the scheduling of complete blood cell counts and chemistry and coagulation panels to a 24-hour interval would reduce laboratory utilization and associated costs. METHODS: We performed a retrospective analysis of these initiatives in a pediatric cardiovascular ICU (CVICU) that included all patients with congenital or acquired heart disease admitted to the cardiovascular ICU from September 1, 2008, until April 1, 2011. Our primary outcomes were the number of laboratory orders and cost of laboratory orders. Our secondary outcomes were mortality and CVICU and hospital length of stay. RESULTS: We found a reduction in laboratory utilization frequency in the checklist intervention period and additional reduction in the CPOE intervention period [complete blood count: 31% and 44% (P < .0001); comprehensive chemistry panel: 48% and 72% (P < .0001); coagulation panel: 26% and 55% (P < .0001); point of care blood gas: 43% and 44% (P < .0001)] compared with the preintervention period. Projected yearly cost reduction was

Embedding time-limited laboratory orders within computerized provider order entry reduces laboratory utilization.

717, 538.8. There was no change in adjusted mortality rate (odds ratio 1.1, 95% confidence interval 0.7–1.9, P = .65). CVICU and total length of stay (days) was similar in the pre- and postintervention periods. CONCLUSIONS: Use of a daily checklist and CPOE rule reduced laboratory resource utilization and cost without adversely affecting adjusted mortality or length of stay. CPOE has the potential to hardwire resource management interventions to augment and sustain the daily checklist.

Web-based Real-Time Case Finding for the Population Health Management of Patients With Diabetes Mellitus: A Prospective Validation of the Natural Language Processing–Based Algorithm With Statewide Electronic Medical Records

Objectives: To test the hypothesis that limits on repeating laboratory studies within computerized provider order entry decrease laboratory utilization. Design: Cohort study with historical controls. Setting: A 20-bed PICU in a freestanding, quaternary care, academic children’s hospital. Patients: This study included all patients admitted to the pediatric ICU between January 1, 2008, and December 31, 2009. A total of 818 discharges were evaluated prior to the intervention (January 1, 2008, through December 31, 2008) and 1,021 patient discharges were evaluated postintervention (January 1, 2009, through December 31, 2009). Intervention: A computerized provider order entry rule limited the ability to schedule repeating complete blood cell counts, chemistry, and coagulation studies to a 24-hour interval in the future. The time limit was designed to ensure daily evaluation of the utility of each test. Measurements and Main Results: Initial analysis with t tests showed significant decreases in tests per patient day in the postintervention period (complete blood cell counts: 1.5 ± 0.1 to 1.0 ± 0.1; chemistry: 10.6 ± 0.9 to 6.9 ± 0.6; coagulation: 3.3 ± 0.4 to 1.7 ± 0.2; p < 0.01, all variables vs. preintervention period). Even after incorporating a trend toward decreasing laboratory utilization in the preintervention period into our regression analysis, the intervention decreased complete blood cell counts (p = 0.007), chemistry (p = 0.049), and coagulation (p = 0.001) tests per patient day. Conclusions: Limits on laboratory orders within the context of computerized provider order entry decreased laboratory utilization without adverse affects on mortality or length of stay. Broader application of this strategy might decrease costs, the incidence of iatrogenic anemia, and catheter-associated bloodstream infections.

Development, Validation and Deployment of a Real Time 30 Day Hospital Readmission Risk Assessment Tool in the Maine Healthcare Information Exchange

Background Diabetes case finding based on structured medical records does not fully identify diabetic patients whose medical histories related to diabetes are available in the form of free text. Manual chart reviews have been used but involve high labor costs and long latency. Objective This study developed and tested a Web-based diabetes case finding algorithm using both structured and unstructured electronic medical records (EMRs). Methods This study was based on the health information exchange (HIE) EMR database that covers almost all health facilities in the state of Maine, United States. Using narrative clinical notes, a Web-based natural language processing (NLP) case finding algorithm was retrospectively (July 1, 2012, to June 30, 2013) developed with a random subset of HIE-associated facilities, which was then blind tested with the remaining facilities. The NLP-based algorithm was subsequently integrated into the HIE database and validated prospectively (July 1, 2013, to June 30, 2014). Results Of the 935,891 patients in the prospective cohort, 64,168 diabetes cases were identified using diagnosis codes alone. Our NLP-based case finding algorithm prospectively found an additional 5756 uncodified cases (5756/64,168, 8.97% increase) with a positive predictive value of .90. Of the 21,720 diabetic patients identified by both methods, 6616 patients (6616/21,720, 30.46%) were identified by the NLP-based algorithm before a diabetes diagnosis was noted in the structured EMR (mean time difference = 48 days). Conclusions The online NLP algorithm was effective in identifying uncodified diabetes cases in real time, leading to a significant improvement in diabetes case finding. The successful integration of the NLP-based case finding algorithm into the Maine HIE database indicates a strong potential for application of this novel method to achieve a more complete ascertainment of diagnoses of diabetes mellitus.

Programmatic Approach to Management of Tetralogy of Fallot With Major Aortopulmonary Collateral Arteries: A 15-Year Experience With 458 Patients

Objectives Identifying patients at risk of a 30-day readmission can help providers design interventions, and provide targeted care to improve clinical effectiveness. This study developed a risk model to predict a 30-day inpatient hospital readmission for patients in Maine, across all payers, all diseases and all demographic groups. Methods Our objective was to develop a model to determine the risk for inpatient hospital readmission within 30 days post discharge. All patients within the Maine Health Information Exchange (HIE) system were included. The model was retrospectively developed on inpatient encounters between January 1, 2012 to December 31, 2012 from 24 randomly chosen hospitals, and then prospectively validated on inpatient encounters from January 1, 2013 to December 31, 2013 using all HIE patients. Results A risk assessment tool partitioned the entire HIE population into subgroups that corresponded to probability of hospital readmission as determined by a corresponding positive predictive value (PPV). An overall model c-statistic of 0.72 was achieved. The total 30-day readmission rates in low (score of 0–30), intermediate (score of 30–70) and high (score of 70–100) risk groupings were 8.67%, 24.10% and 74.10%, respectively. A time to event analysis revealed the higher risk groups readmitted to a hospital earlier than the lower risk groups. Six high-risk patient subgroup patterns were revealed through unsupervised clustering. Our model was successfully integrated into the statewide HIE to identify patient readmission risk upon admission and daily during hospitalization or for 30 days subsequently, providing daily risk score updates. Conclusions The risk model was validated as an effective tool for predicting 30-day readmissions for patients across all payer, disease and demographic groups within the Maine HIE. Exposing the key clinical, demographic and utilization profiles driving each patient’s risk of readmission score may be useful to providers in developing individualized post discharge care plans.

NLP based congestive heart failure case finding: A prospective analysis on statewide electronic medical records

Background— Tetralogy of Fallot with major aortopulmonary collateral arteries is a complex and heterogeneous condition. Our institutional approach to this lesion emphasizes early complete repair with the incorporation of all lung segments and extensive lobar and segmental pulmonary artery reconstruction. Methods and Results— We reviewed all patients who underwent surgical intervention for tetralogy of Fallot and major aortopulmonary collateral arteries at Lucile Packard Children’s Hospital Stanford (LPCHS) since November 2001. A total of 458 patients underwent surgery, 291 (64%) of whom underwent their initial procedure at LPCHS. Patients were followed for a median of 2.7 years (mean 4.3 years) after the first LPCHS surgery, with an estimated survival of 85% at 5 years after first surgical intervention. Factors associated with worse survival included first LPCHS surgery type other than complete repair and Alagille syndrome. Of the overall cohort, 402 patients achieved complete unifocalization and repair, either as a single-stage procedure (n=186), after initial palliation at our center (n=74), or after surgery elsewhere followed by repair/revision at LPCHS (n=142). The median right ventricle:aortic pressure ratio after repair was 0.35. Estimated survival after repair was 92.5% at 10 years and was shorter in patients with chromosomal anomalies, older age, a greater number of collaterals unifocalized, and higher postrepair right ventricle pressure. Conclusions— Using an approach that emphasizes early complete unifocalization and repair with incorporation of all pulmonary vascular supply, we have achieved excellent results in patients with both native and previously operated tetralogy of Fallot and major aortopulmonary collateral arteries.

Real-time web-based assessment of total population risk of future emergency department utilization: statewide prospective active case finding study.

BACKGROUND In order to proactively manage congestive heart failure (CHF) patients, an effective CHF case finding algorithm is required to process both structured and unstructured electronic medical records (EMR) to allow complementary and cost-efficient identification of CHF patients. METHODS AND RESULTS We set to identify CHF cases from both EMR codified and natural language processing (NLP) found cases. Using narrative clinical notes from all Maine Health Information Exchange (HIE) patients, the NLP case finding algorithm was retrospectively (July 1, 2012-June 30, 2013) developed with a random subset of HIE associated facilities, and blind-tested with the remaining facilities. The NLP based method was integrated into a live HIE population exploration system and validated prospectively (July 1, 2013-June 30, 2014). Total of 18,295 codified CHF patients were included in Maine HIE. Among the 253,803 subjects without CHF codings, our case finding algorithm prospectively identified 2411 uncodified CHF cases. The positive predictive value (PPV) is 0.914, and 70.1% of these 2411 cases were found to be with CHF histories in the clinical notes. CONCLUSIONS A CHF case finding algorithm was developed, tested and prospectively validated. The successful integration of the CHF case findings algorithm into the Maine HIE live system is expected to improve the Maine CHF care.

Online Prediction of Health Care Utilization in the Next Six Months Based on Electronic Health Record Information: A Cohort and Validation Study.

Background An easily accessible real-time Web-based utility to assess patient risks of future emergency department (ED) visits can help the health care provider guide the allocation of resources to better manage higher-risk patient populations and thereby reduce unnecessary use of EDs. Objective Our main objective was to develop a Health Information Exchange-based, next 6-month ED risk surveillance system in the state of Maine. Methods Data on electronic medical record (EMR) encounters integrated by HealthInfoNet (HIN), Maine’s Health Information Exchange, were used to develop the Web-based surveillance system for a population ED future 6-month risk prediction. To model, a retrospective cohort of 829,641 patients with comprehensive clinical histories from January 1 to December 31, 2012 was used for training and then tested with a prospective cohort of 875,979 patients from July 1, 2012, to June 30, 2013. Results The multivariate statistical analysis identified 101 variables predictive of future defined 6-month risk of ED visit: 4 age groups, history of 8 different encounter types, history of 17 primary and 8 secondary diagnoses, 8 specific chronic diseases, 28 laboratory test results, history of 3 radiographic tests, and history of 25 outpatient prescription medications. The c-statistics for the retrospective and prospective cohorts were 0.739 and 0.732 respectively. Integration of our method into the HIN secure statewide data system in real time prospectively validated its performance. Cluster analysis in both the retrospective and prospective analyses revealed discrete subpopulations of high-risk patients, grouped around multiple “anchoring” demographics and chronic conditions. With the Web-based population risk-monitoring enterprise dashboards, the effectiveness of the active case finding algorithm has been validated by clinicians and caregivers in Maine. Conclusions The active case finding model and associated real-time Web-based app were designed to track the evolving nature of total population risk, in a longitudinal manner, for ED visits across all payers, all diseases, and all age groups. Therefore, providers can implement targeted care management strategies to the patient subgroups with similar patterns of clinical histories, driving the delivery of more efficient and effective health care interventions. To the best of our knowledge, this prospectively validated EMR-based, Web-based tool is the first one to allow real-time total population risk assessment for statewide ED visits.

The Boston Marathon Study: a novel approach to research during residency.

Background The increasing rate of health care expenditures in the United States has placed a significant burden on the nation’s economy. Predicting future health care utilization of patients can provide useful information to better understand and manage overall health care deliveries and clinical resource allocation. Objective This study developed an electronic medical record (EMR)-based online risk model predictive of resource utilization for patients in Maine in the next 6 months across all payers, all diseases, and all demographic groups. Methods In the HealthInfoNet, Maine’s health information exchange (HIE), a retrospective cohort of 1,273,114 patients was constructed with the preceding 12-month EMR. Each patient’s next 6-month (between January 1, 2013 and June 30, 2013) health care resource utilization was retrospectively scored ranging from 0 to 100 and a decision tree–based predictive model was developed. Our model was later integrated in the Maine HIE population exploration system to allow a prospective validation analysis of 1,358,153 patients by forecasting their next 6-month risk of resource utilization between July 1, 2013 and December 31, 2013. Results Prospectively predicted risks, on either an individual level or a population (per 1000 patients) level, were consistent with the next 6-month resource utilization distributions and the clinical patterns at the population level. Results demonstrated the strong correlation between its care resource utilization and our risk scores, supporting the effectiveness of our model. With the online population risk monitoring enterprise dashboards, the effectiveness of the predictive algorithm has been validated by clinicians and caregivers in the State of Maine. Conclusions The model and associated online applications were designed for tracking the evolving nature of total population risk, in a longitudinal manner, for health care resource utilization. It will enable more effective care management strategies driving improved patient outcomes.

Haemodynamic profiles of children with end-stage heart failure

Resident physicians from a pediatric academic training program developed a hospital-wide research project in an effort to enhance their residency research experience. In this model, residents themselves assumed primary responsibility for each stage of a large prospective clinical research study. The project, which was integrated successfully into the residency program, enabled a large group of residents, with mentorship from a dedicated faculty member, to benefit from a structured clinical research experience while providing the flexibility necessary to meet the demands of a busy residency curriculum. Careful topic selection with a well-defined end point, faculty involvement, resident collegiality, and institutional support were factors identified by study leaders as central to the success of this model.