EGEMS (Washington, DC)最新文献

Context: Communication of genetic laboratory results to patients and providers is impeded by the complexity of results and reports. This can lead to misinterpretation of results, causing inappropriate care. Patients often do not receive a copy of the report leading to possible miscommunication. To address these problems, we conducted patient-centered research to inform design of interpretive reports. Here we describe the development and deployment of a specific patient-centered clinical decision support (CDS) tool, a multi-use patient-centered genomic test report (PGR) that interfaces with an electronic health record (EHR).

Implementation process: A PGR with a companion provider report was configured for implementation within the EHR using locally developed software (COMPASS™) to manage secure data exchange and access.

Findings: We conducted semi-structured interviews with patients, family members, and clinicians that showed they sought clear information addressing findings, family implications, resources, prognosis and next steps relative to the genomic result. Providers requested access to applicable, available clinical guidelines. Initial results indicated patients and providers found the PGR contained helpful, valuable information and would provide a basis for result-related conversation between patients, providers and family.

Major themes: Direct patient involvement in the design and development of a PGR identified format and presentation preferences, and delivery of relevant information to patients and providers, prompting the creation of a CDS tool.

Conclusions: Research and development of patient-centered CDS tools designed to support improved patient outcomes, are enhanced by early and substantial engagement of patients in contributing to all phases of tool design and development.

Background: Electronic health record (EHR) based research in oncology can be limited by missing data and a lack of structured data elements. Clinical research data warehouses for specific cancer types can enable the creation of more robust research cohorts.

Methods: We linked data from the Stanford University EHR with the Stanford Cancer Institute Research Database (SCIRDB) and the California Cancer Registry (CCR) to create a research data warehouse for prostate cancer. The database was supplemented with information from clinical trials, natural language processing of clinical notes and surveys on patient-reported outcomes.

Results: 11,898 unique prostate cancer patients were identified in the Stanford EHR, of which 3,936 were matched to the Stanford cancer registry and 6153 in the CCR. 7158 patients with EHR data and at least one of SCIRDB and CCR data were initially included in the warehouse.

Conclusions: A disease-specific clinical research data warehouse combining multiple data sources can facilitate secondary data use and enhance observational research in oncology.

This commentary introduces the Patient-Centered Clinical Decision Support (PCCDS) Learning Network, which is collaborating with AcademyHealth to publish "Better Decisions Together" as part of eGEMs. Patient-centered clinical decision support (CDS) is an important vehicle to address broad issues in the U.S. health care system regarding quality and safety while also achieving better outcomes and better patient and provider satisfaction. Defined as CDS that supports individual patients and their care givers and/or care teams in health-related decisions and actions, PCCDS is an important step forward in advancing endeavors to move patient-centered care forward. The PCCDS Learning Network has developed a framework, referred to as the Analytic Framework for Action (AFA), to organize thinking and activities around PCCDS. A wide array of activities the PCCDS Learning Network is engaging in to inform and connect stakeholders is discussed.

Aim: This study was performed to better characterize accessibility to electronic health records (EHRs) among informatics professionals in various roles, settings, and organizations across the United States and internationally.

Background: The EHR landscape has evolved significantly in recent years, though challenges remain in key areas such as usability. While patient access to electronic health information has gained more attention, levels of access among informatics professionals, including those conducting usability research, have not been well described in the literature. Ironically, many informatics professionals whose aim is to improve EHR design have restrictions on EHR access or publication, which interfere with broad dissemination of findings in areas of usability research.

Methods: To quantify the limitations on EHR access and publication rights, we conducted a survey of informatics professionals from a broad spectrum of roles including practicing clinicians, researchers, administrators, and members of industry. Results were analyzed and levels of EHR access were stratified by role, organizational affiliation, geographic region, EHR type, and restrictions with regard to publishing results of usability testing, including screenshots.

Results: 126 respondents completed the survey, representing all major geographic regions in the United States. 71.5 percent of participants reported some level of EHR access, while 13 percent reported no access whatsoever. Rates of no-access were higher among faculty members and researchers (19 percent). Among faculty members and researchers, 72 percent could access the EHR for usability and/or research purposes, but, of those, fewer than 1 in 3 could freely publish screenshots with results of usability testing and half could not publish such data at all. Across users from all roles, only 21 percent reported the ability to publish screenshots freely without restrictions.

Conclusions: This study offers insight into current patterns of EHR accessibility among informatics professionals, highlighting restrictions that limit dissemination of usability research and testing. Further conversations and shared responsibility among the various stakeholders in industry, government, health care organizations, and informatics professionals are vital to continued EHR optimization.

Background: Health services and implementation researchers often seek to capture the implementation process of complex interventions yet explicit guidance on how to capture this process is limited. Medical record review is a commonly used methodology, especially when used as a proxy for provider behavior, with recognized benefits and limitations. The purpose of this study was to test the feasibility of chart review to measure implementation and offer recommendations for future researchers using this method to capture the implementation process.

Methods: Grounded in qualitative research methods, we measured the implementation of a transitional care intervention for older adults with dementia being discharged from the hospital. We adapted the operationalization of the intervention's components to suit chart review methods, sought input from hospital providers before and after data collection, and assessed the agreement between the results of our chart review and provider-report.

Findings: We believe chart review can be used effectively as a method for capturing the implementation process and provide future researchers with a list of recommendations based on our experience including understanding the nuance between data extraction versus data abstraction, allowing for large amounts of data not pre-specified in the data collection instrument to be collected, and purposefully and iteratively engaging the providers who are entering data into the chart.

Major themes: Measuring the implementation of complex interventions is a cornerstone in health services research and with the relative convenience and low costs of using chart data, we believe with more use and refinement this methodology could emerge as a valuable and widely used method in the field.

Background: The availability of high fidelity electronic health record (EHR) data is a hallmark of the learning health care system. Washington State's Surgical Care Outcomes and Assessment Program (SCOAP) is a network of hospitals participating in quality improvement (QI) registries wherein data are manually abstracted from EHRs. To create the Comparative Effectiveness Research and Translation Network (CERTAIN), we semi-automated SCOAP data abstraction using a centralized federated data model, created a central data repository (CDR), and assessed whether these data could be used as real world evidence for QI and research.

Objectives: Describe the validation processes and complexities involved and lessons learned.

Methods: Investigators installed a commercial CDR to retrieve and store data from disparate EHRs. Manual and automated abstraction systems were conducted in parallel (10/2012-7/2013) and validated in three phases using the EHR as the gold standard: 1) ingestion, 2) standardization, and 3) concordance of automated versus manually abstracted cases. Information retrieval statistics were calculated.

Results: Four unaffiliated health systems provided data. Between 6 and 15 percent of data elements were abstracted: 51 to 86 percent from structured data; the remainder using natural language processing (NLP). In phase 1, data ingestion from 12 out of 20 feeds reached 95 percent accuracy. In phase 2, 55 percent of structured data elements performed with 96 to 100 percent accuracy; NLP with 89 to 91 percent accuracy. In phase 3, concordance ranged from 69 to 89 percent. Information retrieval statistics were consistently above 90 percent.

Conclusions: Semi-automated data abstraction may be useful, although raw data collected as a byproduct of health care delivery is not immediately available for use as real world evidence. New approaches to gathering and analyzing extant data are required.

Introduction: Patient Aligned Care Team (PACT) care managers are tasked with identifying aging Veterans with psychiatric disease in attempt to prevent psychiatric crises. However, few resources exist that use real-time information on patient risk to prioritize coordinating appropriate care amongst a complex aging population.

Objective: To develop and validate a model to predict psychiatric hospital admission, during a 90-day risk window, in Veterans ages 65 or older with a history of mental health disease.

Methods: This study applied a cohort design to historical data available in the Veterans Affairs (VA) Corporate Data Warehouse (CDW). The Least Absolute Shrinkage and Selection Operator (LASSO) regularization regression technique was used for model development and variable selection. Individual predicted probabilities were estimated using logistic regression. A split-sample approach was used in performing external validation of the fitted model. The concordance statistic (C-statistic) was calculated to assess model performance.

Results: Prior to modeling, 61 potential candidate predictors were identified and 27 variables remained after applying the LASSO method. The final model's predictive accuracy is represented by a C-statistic of 0.903. The model's predictive accuracy during external validation is represented by a C-statistic of 0.935. Having a previous psychiatric hospitalization, psychosis, bipolar disorder, and the number of mental-health related social work encounters were strong predictors of a geriatric psychiatric hospitalization.

Conclusion: This predictive model is capable of quantifying the risk of a geriatric psychiatric hospitalization with acceptable performance and allows for the development of interventions that could potentially reduce such risk.

Context: Patient reported outcomes (PROs) are one means of systematically gathering meaningful subjective information for patient care, population health, and patient centered outcomes research. However, optimal data management for effective PRO applications is unclear.

Case description: Delivery systems associated with the Health Care Systems Research Network (HCSRN) have implemented PRO data collection as part of the Medicare annual Health Risk Assessment (HRA). A questionnaire assessed data content, collection, storage, and extractability in HCSRN delivery systems.

Findings: Responses were received from 15 (83.3 percent) of 18 sites. The proportion of Medicare beneficiaries completing an HRA ranged from less than 10 to 42 percent. Most sites collected core HRA elements and 10 collected information on additional domains such as social support. Measures for core domains varied across sites. Data were collected at and prior to visits. Modes included paper, clinician entry, patient portals, and interactive voice response. Data were stored in the electronic health record (EHR) in scanned documents, free text, and discrete fields, and in summary databases.

Major themes: PRO implementation requires effectively collecting, storing, extracting, and applying patient-reported data. Standardizing PRO measures and storing data in extractable formats can facilitate multi-site uses for PRO data, while access to individual PROs in the EHR may be sufficient for use at the point of care.

Conclusion: Collecting comparable PRO data elements, storing data in extractable fields, and collecting data from a higher proportion of eligible respondents represents an optimal approach to support multi-site applications of PRO information.

This case study describes the use of multiple administrative data sources within a large, integrated health care delivery system to understand opioid prescribing patterns across practice settings. We describe the information needed to understand prescribing patterns and target interventions, the process for identifying relevant institutional data sources that could be linked to provide information on the settings for prescriptions, and the lessons learned in developing, testing, and implementing an algorithm to link the data sources in a useful manner.