Long-Term Impact of an Electronic Health Record-Enabled, Team-Based, and Scalable Population Health Strategy Based on the Chronic Care Model.

The Chronic Care Model (CCM) is a promising framework for improving population health, but little is known regarding the long-term impact of scalable, informatics-enabled interventions based on this model. To address this challenge, this study evaluated the long-term impact of implementing a scalable, electronic health record (EHR)- enabled, and CCM-based population health program to replace a labor-intensive legacy program in 18 primary care practices. Interventions included point-of-care decision support, quality reporting, team-based care, patient engagement, and provider education. Among 6,768 patients with diabetes receiving care over 4 years, hemoglobin A1c levels remained stable during the 2-year pre-intervention and post-intervention periods (0.03% and 0% increases, respectively), compared to a 0.42% increase expected based on A1c progression observed in the United Kingdom Prospective Diabetes Study long-term outcomes cohort. The results indicate that an EHR-enabled, team- based, and scalable population health strategy based on the CCM may be effective and efficient for managing population health.

Modular design, application architecture, and usage of a self-service model for enterprise data delivery: the Duke Enterprise Data Unified Content Explorer (DEDUCE).

PURPOSE: Data generated in the care of patients are widely used to support clinical research and quality improvement, which has hastened the development of self-service query tools. User interface design for such tools, execution of query activity, and underlying application architecture have not been widely reported, and existing tools reflect a wide heterogeneity of methods and technical frameworks. We describe the design, application architecture, and use of a self-service model for enterprise data delivery within Duke Medicine. METHODS: Our query platform, the Duke Enterprise Data Unified Content Explorer (DEDUCE), supports enhanced data exploration, cohort identification, and data extraction from our enterprise data warehouse (EDW) using a series of modular environments that interact with a central keystone module, Cohort Manager (CM). A data-driven application architecture is implemented through three components: an application data dictionary, the concept of "smart dimensions", and dynamically-generated user interfaces. RESULTS: DEDUCE CM allows flexible hierarchies of EDW queries within a grid-like workspace. A cohort "join" functionality allows switching between filters based on criteria occurring within or across patient encounters. To date, 674 users have been trained and activated in DEDUCE, and logon activity shows a steady increase, with variability between months. A comparison of filter conditions and export criteria shows that these activities have different patterns of usage across subject areas. CONCLUSIONS: Organizations with sophisticated EDWs may find that users benefit from development of advanced query functionality, complimentary to the user interfaces and infrastructure used in other well-published models. Driven by its EDW context, the DEDUCE application architecture was also designed to be responsive to source data and to allow modification through alterations in metadata rather than programming, allowing an agile response to source system changes.

DEDUCE Clinical Text: An Ontology-based Module to Support Self-Service Clinical Notes Exploration and Cohort Development.

Large amounts of information, as well as opportunities for informing research, education, and operations, are contained within clinical text such as radiology reports and pathology reports. However, this content is less accessible and harder to leverage than structured, discrete data. We report on an extension to the Duke Enterprise Data Unified Content Explorer (DEDUCE), a self-service query tool developed to provide clinicians and researchers with access to data within the Duke Medicine Enterprise Data Warehouse (EDW). The DEDUCE Clinical Text module supports ontology-based text searching, enhanced filtering capabilities based on document attributes, and integration of clinical text with structured data and cohort development. The module is implemented with open-source tools extensible to other institutions, including a Java-based search engine (Apache Solr) with complementary full-text indexing library (Lucene) employed with a negation engine (NegEx) modified by clinical users to include to local domain-specific negation phrases.

Framework for Curating and Applying Data Elements within Continuing Use Data: A Case Study from the Durham Diabetes Coalition.

Data within a continuing use context (also known as secondary use) can require translation into the variables necessary for project analysis. We have developed and applied a framework in which: Project objectives inform the curation of data elements. Data elements are rendered into system-readable metadata. Metadata are applied to the source data and used to produce data sets. This process distinguishes between data sets and source data. Data sets contain project-specific variables that are structured for analytic activities. This can differ from source data, which may be stored in a structure dictated by the original source system for data collection, or in a data structure contrary to what is desired for analysis. Data elements mediate this translation, and the process of curation refines their definitions and associated attributes. This framework improves analysis workflow through the application of best practices, consistent processes, and centralized decision-making.

Resident education in the era of patient safety: a nationwide analysis of outcomes and complications in resident-assisted oncologic surgery.

BACKGROUND: Complex, oncologic surgery is an important component of resident education. Our objective was to evaluate the impact of resident participation in oncologic procedures on overall 30-day morbidity and mortality. METHODS: A retrospective cohort analysis was performed using the National Surgical Quality Improvement Program Participant User Files for 2005-2009. Colorectal, hepatopancreaticobiliary, and gastroesophageal oncology procedures were included. Multivariate logistic regression was used to assess the impact of trainee involvement on 30-day morbidity and mortality after adjusting for potential confounders. RESULTS: A total of 77,862 patients were included for analysis, 53,885 (69.2%) involving surgical trainees and 23,977 (30.8%) without trainees. The overall 30-day morbidity was significantly higher in the trainee group [27.2 vs. 21%, adjusted odds ratio (AOR) 1.19, 95% confidence interval (CI) 1.15-1.24, p < 0.0001)]; however, there was significantly lower 30-day postoperative mortality in the trainee group (1.9 vs. 2.1%, AOR 0.87, 95% CI 0.77-0.98, p = 0.02) and significantly lower failure-to-rescue rate (defined as mortality rate among patients suffering one or more postoperative complications) (5.9 vs. 7.6%, AOR 0.79, 95% CI 0.68-0.90, p = 0.001). The overall 30-day morbidity was highest in the PGY 5 level (29%) compared to 24% for PGY 1 or 2 and 23% for PGY 3 (AOR per level increase 1.05, 95% CI 1.03-1.07, p < 0.0001). CONCLUSIONS: Trainee participation in complex, oncologic surgery is associated with significantly higher rates of 30-day postoperative complications in NSQIP-participating hospitals; however, this effect is countered by overall lower 30-day mortality and improved rescue rate in preventing death among patients suffering complications.

Does access modality matter? Evaluation of validity in reusing clinical care data.

Self-service database portals may improve access to institutional data resources for clinical research or quality improvement, but questions remain about the validity of this approach. We tested the accuracy of data extracted from a clinical data repository using a self-service portal by comparing three approaches to measuring medication use among patients with coronary disease: (1) automated extraction using a portal, (2) extraction by an experienced data architect, and (3) manual chart abstraction. Outcomes included medications and diagnoses (e.g., myocardial infarction, heart failure). Charts were manually reviewed for 200 patients. Using matched criteria, self-service query identified 7327 of 7358 patients identified by the data analyst. For patients in both cohorts, agreement rates ranged from 0.99 for demographic data to 0.94 for laboratory data. Based on chart review, the self-service portal and the analyst had similar sensitivities and specificities for comorbid diagnoses and statin use.

Risk factors for infection after orthopaedic spine surgery at a high-volume institution.

Surgical site infections are associated with increased morbidity, mortality, and resource utilization. To identify risk factors for infection, the authors reviewed all orthopaedic spine operations at Duke University Medical Center from 2005 to 2010. Of the 3138 patients treated during the study period, 115 developed a surgical site infection (3.7%). Demographics, comorbidities, and perioperative blood glucose levels were analyzed in the infected and uninfected cohorts. History of myocardial infarction, congestive heart failure, renal disease, pneumonia, urinary tract infection, and diabetes mellitus (DM) were associated with a higher risk of infection. Notably, a diagnosis of DM nearly doubled the risk of infection. Even in patients without DM, perioperative blood glucose levels greater than 140 mg/dL doubled the risk of infection. The authors propose that strict blood glucose control in both DM and non-DM patients may significantly reduce the risk of infection after spinal surgery.

The design and implementation of an open-source, data-driven cohort recruitment system: the Duke Integrated Subject Cohort and Enrollment Research Network (DISCERN).

OBJECTIVE: Failure to reach research subject recruitment goals is a significant impediment to the success of many clinical trials. Implementation of health-information technology has allowed retrospective analysis of data for cohort identification and recruitment, but few institutions have also leveraged real-time streams to support such activities. DESIGN: Duke Medicine has deployed a hybrid solution, The Duke Integrated Subject Cohort and Enrollment Research Network (DISCERN), that combines both retrospective warehouse data and clinical events contained in prospective Health Level 7 (HL7) messages to immediately alert study personnel of potential recruits as they become eligible. RESULTS: DISCERN analyzes more than 500000 messages daily in service of 12 projects. Users may receive results via email, text pages, or on-demand reports. Preliminary results suggest DISCERN's unique ability to reason over both retrospective and real-time data increases study enrollment rates while reducing the time required to complete recruitment-related tasks. The authors have introduced a preconfigured DISCERN function as a self-service feature for users. LIMITATIONS: The DISCERN framework is adoptable primarily by organizations using both HL7 message streams and a data warehouse. More efficient recruitment may exacerbate competition for research subjects, and investigators uncomfortable with new technology may find themselves at a competitive disadvantage in recruitment. CONCLUSION: DISCERN's hybrid framework for identifying real-time clinical events housed in HL7 messages complements the traditional approach of using retrospective warehoused data. DISCERN is helpful in instances when the required clinical data may not be loaded into the warehouse and thus must be captured contemporaneously during patient care. Use of an open-source tool supports generalizability to other institutions at minimal cost.

Reversible retinal edema in an infant with neonatal hemochromatosis and liver failure.

We present a case of bilateral severe retinal edema with subretinal fluid in an infant diagnosed with neonatal hemochromatosis and liver failure. A macular cherry-red spot in each eye mimicked the clinical appearance of many metabolic storage diseases. Both the clinical retinal appearance and the anatomic abnormalities observed on spectral domain optical coherence tomography resolved after successful liver transplant.

Using a computerized provider order entry system to meet the unique prescribing needs of children: description of an advanced dosing model.

BACKGROUND: It is well known that the information requirements necessary to safely treat children with therapeutic medications cannot be met with the same approaches used in adults. Over a 1-year period, Duke University Hospital engaged in the challenging task of enhancing an established computerized provider order entry (CPOE) system to address the unique medication dosing needs of pediatric patients. METHODS: An advanced dosing model (ADM) was designed to interact with our existing CPOE application to provide decision support enabling complex pediatric dose calculations based on chronological age, gestational age, weight, care area in the hospital, indication, and level of renal impairment. Given that weight is a critical component of medication dosing that may change over time, alerting logic was added to guard against erroneous entry or outdated weight information. RESULTS: Pediatric CPOE was deployed in a staggered fashion across 6 care areas over a 14-month period. Safeguards to prevent miskeyed values became important in allowing providers the flexibility to override the ADM logic if desired. Methods to guard against over- and under-dosing were added. The modular nature of our model allows us to easily add new dosing scenarios for specialized populations as the pediatric population and formulary change over time. CONCLUSIONS: The medical needs of pediatric patients vary greatly from those of adults, and the information systems that support those needs require tailored approaches to design and implementation. When a single CPOE system is used for both adults and pediatrics, safeguards such as redirection and suppression must be used to protect children from inappropriate adult medication dosing content. Unlike other pediatric dosing systems, our model provides active dosing assistance and dosing process management, not just static dosing advice.

Bridging the gap: leveraging business intelligence tools in support of patient safety and financial effectiveness.

Healthcare is increasingly dependent upon information technology (IT), but the accumulation of data has outpaced our capacity to use it to improve operating efficiency, clinical quality, and financial effectiveness. Moreover, hospitals have lagged in adopting thoughtful analytic approaches that would allow operational leaders and providers to capitalize upon existing data stores. In this manuscript, we propose a fundamental re-evaluation of strategic IT investments in healthcare, with the goal of increasing efficiency, reducing costs, and improving outcomes through the targeted application of health analytics. We also present three case studies that illustrate the use of health analytics to leverage pre-existing data resources to support improvements in patient safety and quality of care, to increase the accuracy of billing and collection, and support emerging health issues. We believe that such active investment in health analytics will prove essential to realizing the full promise of investments in electronic clinical systems.

The clinical document architecture and the continuity of care record: a critical analysis.

Health care provides many opportunities in which the sharing of data between independent sites is highly desirable. Several standards are required to produce the functional and semantic interoperability necessary to support the exchange of such data: a common reference information model, a common set of data elements, a common terminology, common data structures, and a common transport standard. This paper addresses one component of that set of standards: the ability to create a document that supports the exchange of structured data components. Unfortunately, two different standards development organizations have produced similar standards for that purpose based on different information models: Health Level 7 (HL7)'s Clinical Document Architecture (CDA) and The American Society for Testing and Materials (ASTM International) Continuity of Care Record (CCR). The coexistence of both standards might require mapping from one standard to the other, which could be accompanied by a loss of information and functionality. This paper examines and compares the two standards, emphasizes the strengths and weaknesses of each, and proposes a strategy of harmonization to enhance future progress. While some of the authors are members of HL7 and/or ASTM International, the authors stress that the viewpoints represented in this paper are those of the authors and do not represent the official viewpoints of either HL7 or of ASTM International.