EGEMS (Washington, DC)最新文献

Objectives: Measuring obesity prevalence across geographic areas should account for environmental and socioeconomic factors that contribute to spatial autocorrelation, the dependency of values in estimates across neighboring areas, to mitigate the bias in measures and risk of type I errors in hypothesis testing. Dependency among observations across geographic areas violates statistical independence assumptions and may result in biased estimates. Empirical Bayes (EB) estimators reduce the variability of estimates with spatial autocorrelation, which limits the overall mean square-error and controls for sample bias.

Methods: Using the Colorado Body Mass Index (BMI) Monitoring System, we modeled the spatial autocorrelation of adult (≥ 18 years old) obesity (BMI ≥ 30 kg m²) measurements using patient-level electronic health record data from encounters between January 1, 2009, and December 31, 2011. Obesity prevalence was estimated among census tracts with >=10 observations in Denver County census tracts during the study period. We calculated the Moran's I statistic to test for spatial autocorrelation across census tracts, and mapped crude and EB obesity prevalence across geographic areas.

Results: In Denver County, there were 143 census tracts with 10 or more observations, representing a total of 97,710 adults with a valid BMI. The crude obesity prevalence for adults in Denver County was 29.8 percent (95% CI 28.4-31.1%) and ranged from 12.8 to 45.2 percent across individual census tracts. EB obesity prevalence was 30.2 percent (95% CI 28.9-31.5%) and ranged from 15.3 to 44.3 percent across census tracts. Statistical tests using the Moran's I statistic suggest adult obesity prevalence in Denver County was distributed in a non-random pattern. Clusters of EB obesity estimates were highly significant (alpha=0.05) in neighboring census tracts. Concentrations of obesity estimates were primarily in the west and north in Denver County.

Conclusions: Statistical tests reveal adult obesity prevalence exhibit spatial autocorrelation in Denver County at the census tract level. EB estimates for obesity prevalence can be used to control for spatial autocorrelation between neighboring census tracts and may produce less biased estimates of obesity prevalence.

Introduction: Reducing misdiagnosis has long been a goal of medical informatics. Current thinking has focused on achieving this goal by integrating diagnostic decision support into electronic health records.

Methods: A diagnostic decision support system already in clinical use was integrated into electronic health record systems at two large health systems, after clinician input on desired capabilities. The decision support provided three outputs: editable text for use in a clinical note, a summary including the suggested differential diagnosis with a graphical representation of probability, and a list of pertinent positive and pertinent negative findings (with onsets).

Results: Structured interviews showed widespread agreement that the tool was useful and that the integration improved workflow. There was disagreement among various specialties over the risks versus benefits of documenting intermediate diagnostic thinking. Benefits were most valued by specialists involved in diagnostic testing, who were able to use the additional clinical context for richer interpretation of test results. Risks were most cited by physicians making clinical diagnoses, who expressed concern that a process that generated diagnostic possibilities exposed them to legal liability.

Discussion and conclusion: Reconciling the preferences of the various groups could include saving only the finding list as a patient-wide resource, saving intermediate diagnostic thinking only temporarily, or adoption of professional guidelines to clarify the role of decision support in diagnosis.

Context: Obesity is a significant health issue in the United States that both clinical and public health systems struggle to address. Electronic health record data could help support multi-sectoral interventions to address obesity. Standards have been identified and created to support the electronic exchange of weight-related data across many stakeholder groups.

Case description: The Centers for Disease Control and Prevention initiated a public-private partnership including government, industry, and academic technology partners to develop workflow scenarios and supporting systems to exchange weight-related data through standard transactions. This partnership tested the transmission of data using this newly-defined Healthy Weight (HW) profile at multiple health data interoperability demonstration events.

Findings: Five transaction types were tested by 12 partners who demonstrated how the standards and related systems support end-to-end workflows around managing weight-related issues in the community. The standard transactions were successfully tested at two Integrating the Healthcare Enterprise (IHE) Connectathon events through 86 validated tests encompassing 38 multi-partner transactions.

Discussion: We have successfully demonstrated the transactions defined in the HW profile with a public-private partnership. These tested IT products and HW standards could be used to support a continuum of care around health related issues encompassing both health care and public health functions.

Conclusion: The use of the HW profile, including a set of transactions and identified standards to implement those transactions, in IT products is a helpful first step in leveraging health information technology to address weight-related issues in the United States. Future work is needed to expand the use of these standards and to assess their use in real world settings.

Electronic health record (EHR) data are becoming a primary resource for clinical research. Compared to traditional research data, such as those from clinical trials and epidemiologic cohorts, EHR data have a number of appealing characteristics. However, because they do not have mechanisms set in place to ensure that the appropriate data are collected, they also pose a number of analytic challenges. In this paper, we illustrate that how a patient interacts with a health system influences which data are recorded in the EHR. These interactions are typically informative, potentially resulting in bias. We term the overall set of induced biases informed presence. To illustrate this, we use examples from EHR based analyses. Specifically, we show that: 1) Where a patient receives services within a health facility can induce selection bias; 2) Which health system a patient chooses for an encounter can result in information bias; and 3) Referral encounters can create an admixture bias. While often times addressing these biases can be straightforward, it is important to understand how they are induced in any EHR based analysis.

Objective: To understand the impact of distinct concept to value set mapping on the measurement of quality of care.

Background: Clinical quality measures (CQMs) intend to measure the quality of healthcare services provided, and to help promote evidence-based therapies. Most CQMs consist of grouped codes from vocabularies - or 'value sets' - that represent the unique identifiers (i.e., object identifiers), concepts (i.e., value set names), and concept definitions (i.e., code groups) that define a measure's specifications. In the development of a statin therapy CQM, two unique value sets were created by independent measure developers for the same global concepts.

Methods: We first identified differences between the two value set specifications of the same CQM. We then implemented the various versions in a quality measure calculation registry to understand how the differences affected calculated prevalence of risk and measure performance.

Results: Global performance rates only differed by 0.8%, but there were up to 2.3 times as many patients included with key conditions, and differing performance rates of 7.5% for patients with 'myocardial infarction' and 3.5% for those with 'ischemic vascular disease'.

Conclusion: The decisions CQM developers make about which concepts and code groups to include or exclude in value set vocabularies can lead to inaccuracies in the measurement of quality of care. One solution is that developers could provide rationale for these decisions. Endorsements are needed to encourage system vendors, payers, informaticians, and clinicians to collaborate in the creation of more integrated terminology sets.

Introduction: Electronic health record (EHR) data are known to have significant data quality issues, yet the practice and frequency of assessing EHR data is unknown. We sought to understand current practices and attitudes towards reporting data quality assessment (DQA) results by data professionals.

Methods: The project was conducted in four Phases: (1) examined current DQA practices among informatics/CER stakeholders via engagement meeting (07/2014); (2) characterized organizations conducting DQA by interviewing key personnel and data management professionals (07-08/2014); (3) developed and administered an anonymous survey to data professionals (03-06/2015); and (4) validated survey results during a follow-up informatics/CER stakeholder engagement meeting (06/2016).

Results: The first engagement meeting identified the theme of unintended consequences as a primary barrier to DQA. Interviewees were predominantly medical groups serving distributed networks with formalized DQAs. Consistent with the interviews, most survey (N=111) respondents utilized DQA processes/programs. A lack of resources and clear definitions of how to judge the quality of a dataset were the most commonly cited individual barriers. Vague quality action plans/expectations and data owners not trained in problem identification and problem-solving skills were the most commonly cited organizational barriers. Solutions included allocating resources for DQA, establishing standards and guidelines, and changing organizational culture.

Discussion: Several barriers affecting DQA and reporting were identified. Community alignment towards systematic DQA and reporting is needed to overcome these barriers.

Conclusion: Understanding barriers and solutions to DQA reporting is vital for establishing trust in the secondary use of EHR data for quality improvement and the pursuit of personalized medicine.

Introduction: Short Message Service (SMS) appointment reminders may provide a wide-reaching, low cost approach to reducing operational inefficiencies and improving access to care. Previous studies indicate this modality may improve attendance rates, yet there is a need for large-scale, pragmatic studies that include unintended consequences and operational costs.

Methods: This pragmatic investigation was a before-after analysis that compared visit attendance outcomes among patients who opted into SMS appointment reminders with outcomes among those who declined over an 18-month evaluation period from March 25, 2013, to September 30, 2014. Eligibility in our integrated safety net health care system included age greater than 17, English or Spanish as a primary language, and a cell phone number in our scheduling system.

Results: 47,390 patients were invited by SMS to participate, of which 20,724 (43.7 percent) responded with 18,138 opting in (81.5 percent of respondents). Participants received SMS reminders for 77,783 scheduled visits; comparison group patients (N=72,757) were scheduled for 573,079 visits during the evaluation period. Intervention and comparison groups had, respectively, attendance rates of 72.8 percent versus 66.1 percent (p<0.001), cancellation rates of 13.2 percent versus 18.6 percent (p<0.001), and no show rates of 14.0 percent versus 15.3 percent. Patient satisfaction with text messaging ranged from 77 percent to 96 percent. Implementation challenges included a low rate of inaccurate reminders due to non-standard use of the scheduling system across clinical departments.

Discussion: SMS appointment reminders improve patient satisfaction and provide a low operating cost approach to reducing operational inefficiencies through improved attendance rates in an integrated safety net health care system.

Introduction: We describe the formulation, development, and initial expert review of 3x3 Data Quality Assessment (DQA), a dynamic, evidence-based guideline to enable electronic health record (EHR) data quality assessment and reporting for clinical research.

Methods: 3x3 DQA was developed through the triangulation results from three studies: a review of the literature on EHR data quality assessment, a quantitative study of EHR data completeness, and a set of interviews with clinical researchers. Following initial development, the guideline was reviewed by a panel of EHR data quality experts.

Results: The guideline embraces the task-dependent nature of data quality and data quality assessment. The core framework includes three constructs of data quality: complete, correct, and current data. These constructs are operationalized according to the three primary dimensions of EHR data: patients, variables, and time. Each of the nine operationalized constructs maps to a methodological recommendation for EHR data quality assessment. The initial expert response to the framework was positive, but improvements are required.

Discussion: The initial version of 3x3 DQA promises to enable explicit guideline-based best practices for EHR data quality assessment and reporting. Future work will focus on increasing clarity on how and when 3x3 DQA should be used during the research process, improving the feasibility and ease-of-use of recommendation execution, and clarifying the process for users to determine which operationalized constructs and recommendations are relevant for a given dataset and study.

Introduction: Electronic Health Records (EHRs) and Health Information Exchanges (HIEs) are changing surveillance and analytic operations within local health departments (LHDs) across the United States. The objective of this study was to analyze the status, benefits, barriers, and ways of overcoming challenges in the implementation of EHRs and HIEs in LHDs.

Methods: This study employed a mixed methods approach, first using the 2013 National Profile of LHDs survey to ascertain the status of EHR and HIE implementation across the US, as well as to aid in selection of respondents for the second, interview-based part of project. Next, forty-nine key-informant interviews of local health department staff were conducted. Data were coded thematically and independently by two researchers. Coding was compared and re-coded using the consensus definitions.

Results: Twenty-three percent of LHDs nationwide are using EHRs and 14 percent are using HIEs. The most frequently mentioned benefits for implementation were identified as care coordination, retrieval or managing information, and the ability to track outcomes of care. A few mentioned barriers included financial resources, resistance to change, and IT related issues during implementation.

Discussion: Despite financial, technical capacity, and operational constraints, leaders interviewed as part of this project were optimistic about the future of EHRs in local health departments. Recent policy changes and accreditation have implications of improving processes to affect populations served.

Conclusions: Overcoming the challenges in implementing EHRs can result in increased efficiencies in surveillance and higher quality patient care and tracking. However, significant opportunity cost does exist.

Introduction: Health information exchange (HIE) promises cost and utilization reductions. To date, only a small number of HIE studies have demonstrated benefits to patients, providers, public health, or payers. This may be because evaluations of HIE are methodologically challenging. Indeed, the quality of HIE evaluations is often limited and authors frequently note unmet evaluation objectives. We provide a systematic identification of HIE research challenges that can be used to inform strategies for higher quality scientific evidence.

Methods: We conducted qualitative interviews with 23 HIE researchers and leaders of HIE efforts representing experiences with more than 20 HIE efforts. We also conducted a six-person focus group to expand on and confirm individual interview findings. Qualitative analysis followed a grounded theory approach using multiple coders.

Results: Participants experienced similar challenges across seven themes (i.e., HIE maturity, data quality, data availability, goal alignment, cooperation, methodology, and policy).

Conclusion: Several options may exist to improve HIE research, including developing better conceptual models and methodological approaches to HIE research; formal partnerships between researchers and HIE entities; and establishing a nationwide database of HIE information. Our proposed approaches of promoting data availability, resource sharing, and new partnerships can help to overcome existing barriers and facilitate HIE research.