The Digital Drag and Drop Pillbox: Design and Feasibility of a Skill-based Education Model to Improve Medication Management.

OBJECTIVE: We present the design and feasibility testing for the "Digital Drag and Drop Pillbox" (D-3 Pillbox), a skill-based educational approach that engages patients and providers, measures performance, and generates reports of medication management skills. METHODS: A single-cohort convenience sample of patients hospitalized with heart failure was taught pill management skills using a tablet-based D-3 Pillbox. Medication reconciliation was conducted, and aptitude, performance (% completed), accuracy (% correct), and feasibility were measured. RESULTS: The mean age of the sample (n = 25) was 59 (36-89) years, 50% were women, 62% were black, 46% were uninsured, 46% had seventh-grade education or lower, and 31% scored very low for health literacy. However, most reported that the D-3 Pillbox was easy to read (78%), easy to repeat-demonstrate (78%), and comfortable to use (tablet weight) (75%). Accurate medication recognition was achieved by discharge in 98%, but only 25% reported having a "good understanding of my responsibilities." CONCLUSIONS: The D-3 Pillbox is a feasible approach for teaching medication management skills and can be used across clinical settings to reinforce skills and medication list accuracy.

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.

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.

The DEDUCE Guided Query tool: providing simplified access to clinical data for research and quality improvement.

In many healthcare organizations, comparative effectiveness research and quality improvement (QI) investigations are hampered by a lack of access to data created as a byproduct of patient care. Data collection often hinges upon either manual chart review or ad hoc requests to technical experts who support legacy clinical systems. In order to facilitate this needed capacity for data exploration at our institution (Duke University Health System), we have designed and deployed a robust Web application for cohort identification and data extraction--the Duke Enterprise Data Unified Content Explorer (DEDUCE). DEDUCE is envisioned as a simple, web-based environment that allows investigators access to administrative, financial, and clinical information generated during patient care. By using business intelligence tools to create a view into Duke Medicine's enterprise data warehouse, DEDUCE provides a Guided Query functionality using a wizard-like interface that lets users filter through millions of clinical records, explore aggregate reports, and, export extracts. Researchers and QI specialists can obtain detailed patient- and observation-level extracts without needing to understand structured query language or the underlying database model. Developers designing such tools must devote sufficient training and develop application safeguards to ensure that patient-centered clinical researchers understand when observation-level extracts should be used. This may mitigate the risk of data being misunderstood and consequently used in an improper fashion.