Andrew L. Masica

Challenges in using electronic health record data for CER: experience of 4 learning organizations and solutions applied.

Objective: To document the strengths and challenges of using electronic health records (EHRs) for comparative effectiveness research (CER). Methods: A replicated case study of comparative effectiveness in hypertension treatment was conducted across 4 health systems, with instructions to extract data and document problems encountered using a specified list of required data elements. Researchers at each health system documented successes and challenges, and suggested solutions for addressing challenges. Results: Data challenges fell into 5 categories: missing data, erroneous data, uninterpretable data, inconsistencies among providers and over time, and data stored in noncoded text notes. Suggested strategies to address these issues include data validation steps, use of surrogate markers, natural language processing, and statistical techniques. Discussion: A number of EHR issues can hamper the extraction of valid data for cross-health system comparative effectiveness studies. Our case example cautions against a blind reliance on EHR data as a single definitive data source. Nevertheless, EHR data are superior to administrative or claims data alone, and are cheaper and timelier than clinical trials or manual chart reviews. All 4 participating health systems are pursuing pathways to more effectively use EHR data for CER. A partnership between clinicians, researchers, and information technology specialists is encouraged as a way to capitalize on the wealth of information contained in the EHR. Future developments in both technology and care delivery hold promise for improvement in the ability to use EHR data for CER.

Development and Validation of an Algorithm to Identify Planned Readmissions From Claims Data.

BACKGROUNDnIt is desirable not to include planned readmissions in readmission measures because they represent deliberate, scheduled care.nnnOBJECTIVESnTo develop an algorithm to identify planned readmissions, describe its performance characteristics, and identify improvements.nnnDESIGNnConsensus-driven algorithm development and chart review validation study at 7 acute-care hospitals in 2 health systems.nnnPATIENTSnFor development, all discharges qualifying for the publicly reported hospital-wide readmission measure. For validation, all qualifying same-hospital readmissions that were characterized by the algorithm as planned, and a random sampling of same-hospital readmissions that were characterized as unplanned.nnnMEASUREMENTSnWe calculated weighted sensitivity and specificity, and positive and negative predictive values of the algorithm (version 2.1), compared to gold standard chart review.nnnRESULTSnIn consultation with 27 experts, we developed an algorithm that characterizes 7.8% of readmissions as planned. For validation we reviewed 634 readmissions. The weighted sensitivity of the algorithm was 45.1% overall, 50.9% in large teaching centers and 40.2% in smaller community hospitals. The weighted specificity was 95.9%, positive predictive value was 51.6%, and negative predictive value was 94.7%. We identified 4 minor changes to improve algorithm performance. The revised algorithm had a weighted sensitivity 49.8% (57.1% at large hospitals), weighted specificity 96.5%, positive predictive value 58.7%, and negative predictive value 94.5%. Positive predictive value was poor for the 2 most common potentially planned procedures: diagnostic cardiac catheterization (25%) and procedures involving cardiac devices (33%).nnnCONCLUSIONSnAn administrative claims-based algorithm to identify planned readmissions is feasible and can facilitate public reporting of primarily unplanned readmissions.

A Review of Multifaceted Care Approaches for the Prevention and Mitigation of Delirium in Intensive Care Units.

Objective: The objective of this review is to examine the effectiveness, implementation, and costs of multifaceted care approaches, including care bundles, for the prevention and mitigation of delirium in patients hospitalized in intensive care units (ICUs). Data Sources: A systematic search using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was conducted utilizing PubMed, EMBASE, and CINAHL. Searches were limited to studies published in English from January 1, 1988, to March 31, 2014. Randomized controlled trials and comparative studies of multifaceted care approaches with the reduction of delirium in ICU patients as an outcome and evaluations of the implementation or cost-effectiveness of these interventions were included. Data Extraction: Data on study methods including design, cohort size, interventions, and outcomes were abstracted, reviewed, and summarized. Given the variability in study design, populations, and interventions, a qualitative review of findings was conducted. Data Synthesis: In all, 14 studies met our inclusion criteria: 6 examined outcomes, 5 examined implementation, 2 examined outcomes and implementation, and 1 examined cost-effectiveness. The majority of studies indicated that multifaceted care approaches were associated with improved patient outcomes including reduced incidence and duration of delirium. Additionally, improvements in functional status and reductions in coma and ventilator days, hospital length of stay, and/or mortality rates were observed. Implementation strategies included structured quality improvement approaches with ongoing audit and feedback, multidisciplinary care teams, intensive training, electronic reporting systems, and local support teams. The cost-effectiveness analysis indicated an average reduction of

CER Hub: An informatics platform for conducting comparative effectiveness research using multi-institutional, heterogeneous, electronic clinical data ☆

1000 in hospital costs for patients treated with a multifaceted care approach. Conclusion: Although multifaceted care approaches may reduce delirium and improve patient outcomes, greater improvements may be achieved by deploying a comprehensive bundle of care practices including awakening and breathing trials, delirium monitoring and treatment, and early mobility. Further research to address this knowledge gap is essential to providing best care for ICU patients.

Modifying the Electronic Health Record to Facilitate the Implementation and Evaluation of a Bundled Care Program for Intensive Care Unit Delirium

OBJECTIVESnComparative effectiveness research (CER) requires the capture and analysis of data from disparate sources, often from a variety of institutions with diverse electronic health record (EHR) implementations. In this paper we describe the CER Hub, a web-based informatics platform for developing and conducting research studies that combine comprehensive electronic clinical data from multiple health care organizations.nnnMETHODSnThe CER Hub platform implements a data processing pipeline that employs informatics standards for data representation and web-based tools for developing study-specific data processing applications, providing standardized access to the patient-centric electronic health record (EHR) across organizations.nnnRESULTSnThe CER Hub is being used to conduct two CER studies utilizing data from six geographically distributed and demographically diverse health systems. These foundational studies address the effectiveness of medications for controlling asthma and the effectiveness of smoking cessation services delivered in primary care.nnnDISCUSSIONnThe CER Hub includes four key capabilities: the ability to process and analyze both free-text and coded clinical data in the EHR; a data processing environment supported by distributed data and study governance processes; a clinical data-interchange format for facilitating standardized extraction of clinical data from EHRs; and a library of shareable clinical data processing applications.nnnCONCLUSIONnCER requires coordinated and scalable methods for extracting, aggregating, and analyzing complex, multi-institutional clinical data. By offering a range of informatics tools integrated into a framework for conducting studies using EHR data, the CER Hub provides a solution to the challenges of multi-institutional research using electronic medical record data.

Increasing Health Care Burden of Chronic Liver Disease Compared With Other Chronic Diseases, 2004–2013

Context: Electronic health records (EHRs) have been promoted as a key driver of improved patient care and outcomes and as an essential component of learning health systems. However, to date, many EHRs are not optimized to support delivery of quality and safety initiatives, particularly in Intensive Care Units (ICUs). Delirium is a common and severe problem for ICU patients that may be prevented or mitigated through the use of evidence-based care processes (daily awakening and breathing trials, formal delirium screening, and early mobility—collectively known as the “ABCDE bundle”). This case study describes how an integrated health care delivery system modified its inpatient EHR to accelerate the implementation and evaluation of ABCDE bundle deployment as a safety and quality initiative. Case Description: In order to facilitate uptake of the ABCDE bundle and measure delivery of the care processes within the bundle, we worked with clinical and technical experts to create structured data fields for documentation of bundle elements and to identify where these fields should be placed within the EHR to streamline staff workflow. We created an “ABCDE” tab in the existing patient viewer that allowed providers to easily identify which components of the bundle the patient had and had not received. We examined the percentage of ABCDE bundle elements captured in these structured data fields over time to track compliance with data entry procedures and to improve documentation of care processes. Major Themes: Modifying the EHR to support ABCDE bundle deployment was a complex and time-consuming process. We found that it was critical to gain buy-in from senior leadership on the importance of the ABCDE bundle to secure information technology (IT) resources, understand the different workflows of members of multidisciplinary care teams, and obtain continuous feedback from staff on the EHR revisions during the development cycle. We also observed that it was essential to provide ongoing training to staff on proper use of the new EHR documentation fields. Lastly, timely reporting on ABCDE bundle performance may be essential to improved practice adoption and documentation of care processes. Conclusion: The creation of learning health systems is contingent on an ability to modify EHRs to meet emerging care delivery and quality improvement needs. Although this study focuses on the prevention and mitigation of delirium in ICUs, our process for identifying key data elements and making modifications to the EHR, as well as the lessons learned from the IT components of this program, are generalizable to other health care settings and conditions.

The High Value Healthcare Collaborative: Observational Analyses of Care Episodes for Hip and Knee Arthroplasty Surgery

BACKGROUND & AIMSnChronic liver disease (CLD) is a common and expensive condition, and studies of CLD-related hospitalizations have underestimated the true burden of disease. We analyzed data from a large, diverse health care system to compare time trends in CLD-related hospitalizations with those in congestive heart failure (CHF) or chronic obstructive pulmonary disease (COPD).nnnMETHODSnWe collected data from a large health care system in Texas on hospitalizations related to CLD (nxa0= 27,783), CHF (nxa0= 60,415), and COPD (nxa0= 34,199) from January 1, 2004 through December 31, 2013. We calculated annual hospitalization rates (per 100,000) and compared hospital course, inpatient mortality, ancillary services, and readmissions.nnnRESULTSnCompared with patients with CHF (median age, 71 years) or COPD (median age, 69 years), patients with CLD were significantly younger (median age, 57 years) (P < .01 vs CHF and COPD). Higher proportions of patients with CLD were uninsured (11.7% vs 5.4% for CHF and 5.4% for COPD, P < .01) and Hispanic (17% for CLD vs 9.3% for CHF and 5.0% for COPD, Pxa0< .01). A lower proportion of patients with CLD had Medicare (41.5% vs 68.6% with CHF and 70.1% with COPD, P < .01). From 2004 through 2013, the rate of CLD-related hospitalization increased by 92% (from 1295/100,000 to 2490/100,000), compared with 6.7% for CHF (from 3843/100,000 to 4103/100,000) and 48.8% for COPD (from 1775/100,000 to 2642/100,000). During this time period, CLD-related hospitalizations covered by Medicare increased from 31.8% to 41.5%, whereas hospitalizations covered by Medicare did not change for CHF (remained at 70%) or COPD (remained at 70%). Patients with CLD had longer hospital stays (7.3 days vs 6.2 days for CHF and 5.9 days for COPD, P < .01). A higher proportion of patients with CLD died or were discharged to hospice (14.2% vs 11.5% of patients with CHF and 9.3% of patients with COPD, P < .01), and a smaller proportion had access to postacute care (13.2% vs 23.2% of patients with CHF and 27.4% of patients with COPD, P < .01). A higher proportion of patients with CLD were readmitted to the hospital within 30 days (25% vs 21.9% of patients with CHF and 20.6% with COPD, P < .01).nnnCONCLUSIONSnPatients with CLD, compared with selected other chronic diseases, had increasing rates of hospitalization, longer hospital stays, more readmissions, and, despite these adverse outcomes, less access to postacute care. Disease management models for CLD are greatly needed to manage the anticipated increase in hospitalizations for CLD.

Developing Electronic Data Methods Infrastructure to Participate in Collaborative Research Networks

BACKGROUNDnBroader use of value-based reimbursement models will require providers to transparently demonstrate health care value. We sought to determine and report cost and quality data for episodes of hip and knee arthroplasty surgery among 13 members of the High Value Healthcare Collaborative (HVHC), a consortium of health care systems interested in improving health care value.nnnMETHODSnWe conducted a retrospective, cross-sectional observational cohort study of 30-day episodes of care for hip and knee arthroplasty in fee-for-service Medicare beneficiaries aged 65 or older who had hip or knee osteoarthritis and used 1 of 13 HVHC member systems for uncomplicated primary hip arthroplasty (Nxa0= 8853) or knee arthroplasty (Nxa0= 16,434), respectively, in 2012 or 2013. At the system level, we calculated: per-capita utilization rates; postoperative complication rates; standardized total, acute, and postacute care Medicare expenditures for 30-day episodes of care; and the modeled impact of reducing episode expenditures or per-capita utilization rates.nnnRESULTSnAdjusted per-capita utilization rates varied across HVHC systems and postacute care reimbursements varied more than 3-fold for both types of arthroplasty in both years. Regression analysis confirmed that total episode and postacute care reimbursements significantly differed across HVHC members after considering patient demographic differences. Potential Medicare cost savings were greatest for knee arthroplasty surgery and when lower total reimbursement targets were achieved.nnnCONCLUSIONnThe substantial variation that we found offers opportunities for learning and collaboration to collectively improve outcomes, reduce costs, and enhance value. Ceteris paribus, reducing per-episode reimbursements would achieve greater Medicare cost savings than reducing per-capita rates.

Long-Term Outcomes From Repeated Smoking Cessation Assistance in Routine Primary Care

Context: Collaborative networks support the goals of a learning health system by sharing, aggregating, and analyzing data to facilitate identification of best practices care across delivery organizations. This case study describes the infrastructure and process developed by an integrated health delivery system to successfully prepare and submit a complex data set to a large national collaborative network. Case Description: We submitted four years of data for a diverse population of patients in specific clinical areas: diabetes, chronic heart failure, sepsis, and hip, knee, and spine. The most recent submission included 19 tables, more than 376,000 unique patients, and almost 5 million patient encounters. Data was extracted from multiple clinical and administrative systems. Lessons Learned: We found that a structured process with documentation was key to maintaining communication, timelines, and quality in a large-scale data submission to a national collaborative network. The three key components of this process were the experienced project team, documentation, and communication. We used a formal QA and feedback process to track and review data. Overall, the data submission was resource intensive and required an incremental approach to data quality. Conclusion: Participation in collaborative networks can be time and resource intense, however it can serve as a catalyst to increase the technical data available to the learning health system.

Early inpatient calculation of laboratory-based 30-day readmission risk scores empowers clinical risk modification during index hospitalization

Purpose: To test the association between repeated clinical smoking cessation support and long-term cessation. Design: Retrospective, observational cohort study using structured and free-text data from electronic health records. Setting: Six diverse health systems in the United States. Participants: Patients aged ≥18 years who were smokers in 2007 and had ≥1 primary care visit in each of the following 4 years (N = 33 691). Measures: Primary exposure was a composite categorical variable (comprised of documentation of smoking cessation medication, counseling, or referral) classifying the proportions of visits for which patients received any cessation assistance (<25% (reference), 25%-49%, 50%-74%, and ≥75% of visits). The dependent variable was long-term quit (LTQ; yes/no), defined as no indication of being a current smoker for ≥365 days following a visit where nonsmoker or former smoker was indicated. Analysis: Mixed effects logistic regression analysis adjusted for age, sex, race, and comorbidities, with robust standard error estimation to account for within site correlation. Results: Overall, 20% of the cohort achieved LTQ status. Patients with ≥75% of visits with any assistance had almost 3 times the odds of achieving LTQ status compared to those with <25% visits with assistance (odds ratio = 2.84; 95% confidence interval: 1.50-5.37). Results were similar for specific assistance types. Conclusions: These findings provide support for the importance of repeated assistance at primary care visits to increase long-term smoking cessation.