Project STEP: Implementing the Veterans Health Administration's Stepped Care Model of Pain Management.

Objective: The "stepped care model of pain management" (SCM-PM) prioritizes the role of primary care providers in optimizing pharmacological management and timely and equitable access to patient-centered, evidence-based nonpharmacological approaches, when indicated. Over the past several years, the Veterans Health Administration (VHA) has supported implementation of SCM-PM, but few data exist regarding changes in pain care resulting from implementation. We examined trends in prescribing and referral practices of primary care providers with hypotheses of decreased opioid prescribing, increased nonopioid prescribing, and increased referrals to specialty care for nonpharmacological services. Design: An initiative was designed to foster implementation and systematic evaluation of the SCM-PM over a five-year period at the VA Connecticut Healthcare System (VACHS) while fostering collaborative, partnered initiatives to promote organizational improvements in the delivery of pain care. Subjects: Participants were veterans receiving care at VACHS with at least one pain intensity rating ≥4/10 over the course of the study period (7/2008-6/2013). Methods: We used electronic health record data to examine changes in indicators of pain care including pharmacy and health care utilization data. Results: We observed hypothesized changes in long-term opioid and nonopioid analgesic prescribing and increased utilization of nonpharmacological treatments such as physical therapy, occupational therapy, and clinical health psychology. Conclusions: Through a multifaceted comprehensive implementation approach, primary care providers demonstrated increases in guideline-concordant pain care practices. Findings suggest that engagement of interdisciplinary teams and partnerships to promote organizational improvements is a useful strategy to increase the use of integrated, multimodal pain care for veterans, consistent with VHA's SCM-PM.

Utilizing patient data from the veterans administration electronic health record to support web-based clinical decision support: informatics challenges and issues from three clinical domains.

BACKGROUND: The US Veterans Administration (VA) has developed a robust and mature computational infrastructure in support of its electronic health record (EHR). Web technology offers a powerful set of tools for structuring clinical decision support (CDS) around clinical care. This paper describes informatics challenges and design issues that were confronted in the process of building three Web-based CDS systems in the context of the VA EHR. METHODS: Over the course of several years, we implemented three Web-based CDS systems that extract patient data from the VA EHR environment to provide patient-specific CDS. These were 1) the VACS (Veterans Aging Cohort Study) Index Calculator which estimates prognosis for HIV+ patients, 2) Neuropath/CDS which assists in the medical management of patients with neuropathic pain, and 3) TRIM (Tool to Reduce Inappropriate Medications) which identifies potentially inappropriate medications in older adults and provides recommendations for improving the medication regimen. RESULTS: The paper provides an overview of the VA EHR environment and discusses specific informatics issues/challenges that arose in the context of each of the three Web-based CDS systems. We discuss specific informatics methods and provide details of approaches that may be useful within this setting. CONCLUSIONS: Informatics issues and challenges relating to data access and data availability arose because of the particular architecture of the national VA infrastructure and the need to link to that infrastructure from local Web-based CDS systems. Idiosyncrasies of VA patient data, especially the medication data, also posed challenges. Other issues related to specific functional needs of individual CDS systems. The goal of this paper is to describe these issues so that our experience may serve as a useful foundation to assist others who wish to build such systems in the future.

Disparities in hepatitis C testing in U.S. veterans born 1945-1965.

BACKGROUND & AIMS: Universal one-time antibody testing for hepatitis C virus (HCV) infection has been recommended by the centers for disease control (CDC) and the United States preventative services task force (USPSTF) for Americans born 1945-1965 (birth cohort). Limited data exists addressing national HCV testing practices. We studied patterns and predictors of HCV testing across the U.S. within the birth cohort utilizing data from the national corporate data warehouse of the U.S. Veterans Administration (VA) health system. METHODS: Testing was defined as any HCV test including antibody, RNA or genotype performed during 2000-2013. RESULTS: Of 6,669,388 birth cohort veterans, 4,221,135 (63%) received care within the VA from 2000-2013 with two or more visits. Of this group, 2,139,935 (51%) had HCV testing with 8.1% HCV antibody and 5.4% RNA positive. Significant variation in testing was observed across centers (range: 7-83%). Older, male, African-Americans, with established risk factors and receiving care from urban centers of excellence were more likely to be tested. Among veterans free of other established risk factors (HIV negative, HBV negative, ALT ⩽40U/L, FIB-4 ⩽1.45, or APRI <0.5), HCV antibody and RNA were positive in 2.8% and 0.9%, respectively, comparable to established national average. At least 2.4-4.4% of veterans had scores suggesting advanced fibrosis (APRI ⩾1.5 or FIB-4 >3.25) with >30-43% having positive HCV RNA but >16-20% yet to undergo testing for HCV. CONCLUSIONS: Significant disparities are observed in HCV testing within the United States VA health system. Examination of the predictors of testing and HCV positivity may help inform national screening policies. LAY SUMMARY: Analysis of United States Veterans Administration data show significant disparities in hepatitis C virus testing of veterans born 1945-1965 (birth cohort). A fifth of those not tested had evidence of advanced liver fibrosis. Our data suggests some predictors for this disparity and will potentially help inform future policy measures in the era of universal birth cohort testing for HCV.

Deploying a national clinical text processing infrastructure.

OBJECTIVES: Clinical text processing offers a promising avenue for improving multiple aspects of healthcare, though operational deployment remains a substantial challenge. This case report details the implementation of a national clinical text processing infrastructure within the Department of Veterans Affairs (VA). METHODS: Two foundational use cases, cancer case management and suicide and overdose prevention, illustrate how text processing can be practically implemented at scale for diverse clinical applications using shared services. RESULTS: Insights from these use cases underline both commonalities and differences, providing a replicable model for future text processing applications. CONCLUSIONS: This project enables more efficient initiation, testing, and future deployment of text processing models, streamlining the integration of these use cases into healthcare operations. This project implementation is in a large integrated health delivery system in the United States, but we expect the lessons learned to be relevant to any health system, including smaller local and regional health systems in the United States.

Impact of Hematology Electronic Consultations on Utilization of Referrals and Patient Outcomes in an Integrated Health Care System.

INTRODUCTION: Electronic consultations (e-consults) may be a valuable tool in the current era of increased demand for hematologists. Despite the increasing use of e-consults in hematology, their optimal utilization and impact on patient outcomes and workload are largely unknown. METHODS: In this retrospective cohort study, we studied the hematology consult experience at Veterans Affairs Connecticut from 2006 to 2018. We included 7,664 hematology consults (3,240 e-consults and 4,424 face-to-face [FTF] consults) requested by 1,089 unique clinicians. RESULTS: We found that e-consults were rapidly adopted and used equally among physicians of different degrees of experience. The number of FTF consults did not decrease after the introduction of e-consult services. E-consults were preferentially used for milder laboratory abnormalities that had been less likely to result in a consult before their availability. Referring clinicians used e-consults preferentially for periprocedural management, anemia, leukopenia, and anticoagulation questions. Eighty-three percent of e-consults were resolved without needing an FTF visit in the year after the consult. Consults for pancytopenia, gammopathy, leukocytosis, and for patients with known malignancy were less likely to be resolved by e-consult. Among patients who were diagnosed with a new hematologic malignancy after their consult, having an e-consult before an FTF visit did not adversely affect survival. CONCLUSION: In summary, e-consults safely expanded delivery of hematology services in our health care system but increased total consult volume. We report novel data on what types of consults may be best suited to the electronic modality, the impact of e-consults on workload, and their optimal use and implementation.

Comparison of two VA laboratory data repositories indicates that missing data vary despite originating from the same source.

BACKGROUND: Assessing accuracy and completeness of data is an important component of conducting research. VA Healthcare System benefits from a highly developed electronic medical information system. The Immunology Case Registry was designed to monitor costs and quality of HIV care. The Decision Support System was developed to monitor utilization and costs of veterans in care. Because these systems extract data from the same source using independent methods, they provide an opportunity to compare the accuracy and completeness of each. OBJECTIVE: To compare overlapping laboratory data from the Veterans Affairs Health Information System between 2 data repositories. RESEARCH DESIGN: For hemoglobin, CD4 lymphocyte counts (CD4), HIV RNA viral load, aspartate aminotransferase, alanine aminotransferase, glycosylated hemoglobin, creatinine, and white blood count, we calculated the percent of individuals with a value from each source. For results in both repositories, we calculated Pearson's correlation coefficients. SUBJECTS: A total of 22,647 HIV+ veterans in the Virtual Cohort with a visit in fiscal year 2002. RESULTS: For 6 out of 9 tests, 68% to 72% of the observations overlapped. For CD4, viral load, and glycosylated hemoglobin less than 31% of observations overlapped. Overlapping results were nearly perfectly correlated except for CD4. CONCLUSIONS: Six of the laboratory tests demonstrated remarkably similar amounts of overlap, though Immunology Case Registry and Decision Support System both have missing data. Findings indicate that validation of laboratory data should be conducted before its use in quality and efficiency projects. When 2 databases are not available for comparison, other methods of validation should be implemented.

A Clinical Decision Support System for Monitoring Post-Colonoscopy Patient Follow-Up and Scheduling.

This paper describes a natural language processing (NLP)-based clinical decision support (CDS) system that is geared towards colon cancer care coordinators as the end users. The system is implemented using a metadata- driven Structured Query Language (SQL) function (discriminant function). For our pilot study, we have developed a training corpus consisting of 2,085 pathology reports from the VA Connecticut Health Care System (VACHS). We categorized reports as "actionable"- requiring close follow up, or "non-actionable"- requiring standard or no follow up. We then used 600 distinct pathology reports from 6 different VA sites as our test corpus. Analysis of our test corpus shows that our NLP approach yields 98.5% accuracy in identifying cases that required close clinical follow up. By integrating this into our cancer care tracking system, our goal is to ensure that patients with worrisome pathology receive appropriate and timely follow-up and care.

Pilot of decision support to individualize colorectal cancer screening recommendations.

OBJECTIVES: To test the feasibility of using an electronic medical record (EMR)-based decision support system (DSS) that incorporates morbidity and frailty information to individualize colorectal cancer (CRC) screening recommendations. STUDY DESIGN: Our framework used the payoff time, defined as the minimum time until the benefits of screening exceed the harms. METHODS: Subjects were 24 patients eligible for CRC screening and 22 primary care providers (PCPs). Measures included PCP satisfaction with existing reminder systems and with decision support. RESULTS: The run-in phase, during which the intervention was inactive but its performance was verified, had 14 patients enrolled. The intervention phase, during which payoff time and life expectancy calculations were used to recommend for or against CRC screening, had 10 patients enrolled. Of the 10 patients enrolled in the intervention phase, the DSS recommended in favor of CRC screening for 6 patients. (The PCPs also recommended it for those 6 patients, although 3 refused the screening.) The DSS recommended against CRC screening for 4 patients, while the PCPs recommended against it for 3 of those 4 and ordered the screening for 1 patient. PCPs who had patients enrolled in the intervention phase indicated interest in having payoff time information for all patients eligible for CRC screening. This pilot study was small and was not powered to determine the effect of the intervention on screening behavior. CONCLUSIONS: Colorectal cancer screening involves balancing immediate harms with longer-term benefits; EMR decision support may facilitate personalized benefit/harm assessment. The payoff time framework is feasible for implementation in EMR decision support.

Integrating query of relational and textual data in clinical databases: a case study.

OBJECTIVES: The authors designed and implemented a clinical data mart composed of an integrated information retrieval (IR) and relational database management system (RDBMS). DESIGN: Using commodity software, which supports interactive, attribute-centric text and relational searches, the mart houses 2.8 million documents that span a five-year period and supports basic IR features such as Boolean searches, stemming, and proximity and fuzzy searching. MEASUREMENTS: Results are relevance-ranked using either "total documents per patient" or "report type weighting." RESULTS: Non-curated medical text has a significant degree of malformation with respect to spelling and punctuation, which creates difficulties for text indexing and searching. Presently, the IR facilities of RDBMS packages lack the features necessary to handle such malformed text adequately. CONCLUSION: A robust IR+RDBMS system can be developed, but it requires integrating RDBMSs with third-party IR software. RDBMS vendors need to make their IR offerings more accessible to non-programmers.

Exploring a clinically friendly web-based approach to clinical decision support linked to the electronic health record: design philosophy, prototype implementation, and framework for assessment.

BACKGROUND: Computer-based clinical decision support (CDS) is an important component of the electronic health record (EHR). As an increasing amount of CDS is implemented, it will be important that this be accomplished in a fashion that assists in clinical decision making without imposing unacceptable demands and burdens upon the provider's practice. OBJECTIVE: The objective of our study was to explore an approach that allows CDS to be clinician-friendly from a variety of perspectives, to build a prototype implementation that illustrates features of the approach, and to gain experience with a pilot framework for assessment. METHODS: The paper first discusses the project's design philosophy and goals. It then describes a prototype implementation (Neuropath/CDS) that explores the approach in the domain of neuropathic pain and in the context of the US Veterans Administration EHR. Finally, the paper discusses a framework for assessing the approach, illustrated by a pilot assessment of Neuropath/CDS. RESULTS: The paper describes the operation and technical design of Neuropath/CDS, as well as the results of the pilot assessment, which emphasize the four areas of focus, scope, content, and presentation. CONCLUSIONS: The work to date has allowed us to explore various design and implementation issues relating to the approach illustrated in Neuropath/CDS, as well as the development and pilot application of a framework for assessment.

Development of an electronic medical record-based clinical decision support tool to improve HIV symptom management.

Common symptoms associated with HIV disease and its management are often underrecognized and undertreated. A clinical decision support tool for symptom management was developed within the Veterans Health Administration electronic medical record (EMR), aiming at increasing provider awareness of and response to common HIV symptoms. Its feasibility was studied in March to May 2007 by implementing it within a weekly HIV clinic, comparing a 4-week intervention period with a 4-week control period. Fifty-six patients and their providers participated in the study. Patients' perceptions of providers' awareness of their symptoms, proportion of progress notes mentioning any symptom(s) and proportion of care plans mentioning any symptom(s) were measured. The clinical decision support tool used portable electronic "tablets" to elicit symptom information at the time of check-in, filtered, and organized that information into a concise and clinically relevant EMR note available at the point of care, and facilitated clinical responses to that information. It appeared to be well accepted by patients and providers and did not substantially impact workflow. Although this pilot study was not powered to detect effectiveness, 25 (93%) patients in the intervention group reported that their providers were very aware of their symptoms versus 27 (75%) control patients (p = 0.07). The proportion of providers' notes listing symptoms was similar in both periods; however, there was a trend toward including a greater number of symptoms in intervention period progress notes. The symptom support tool seemed to be useful in clinical HIV care. The Veterans Health Administration EMR may be an effective "laboratory" for developing and testing decision supports.

Measuring performance directly using the veterans health administration electronic medical record: a comparison with external peer review.

BACKGROUND: Electronic medical records systems (EMR) contain many directly analyzable data fields that may reduce the need for extensive chart review, thus allowing for performance measures to be assessed on a larger proportion of patients in care. OBJECTIVE: This study sought to determine the extent to which selected chart review-based clinical performance measures could be accurately replicated using readily available and directly analyzable EMR data. METHODS: A cross-sectional study using full chart review results from the Veterans Health Administration's External Peer Review Program (EPRP) was merged to EMR data. RESULTS: Over 80% of the data on these selected measures found in chart review was available in a directly analyzable form in the EMR. The extent of missing EMR data varied by site of care (P<0.01). Among patients on whom both sources of data were available, we found a high degree of correlation between the 2 sources in the measures assessed (correlations of 0.89-0.98) and in the concordance between the measures using performance cut points (kappa: 0.86-0.99). Furthermore, there was little evidence of bias; the differences in values were not clinically meaningful (difference of 0.9 mg/dL for low-density lipoprotein cholesterol, 1.2 mm Hg for systolic blood pressure, 0.3 mm Hg for diastolic, and no difference for HgbA1c). CONCLUSIONS: Directly analyzable data fields in the EMR can accurately reproduce selected EPRP measures on most patients. We found no evidence of systematic differences in performance values among these with and without directly analyzable data in the EMR.

Home telehealth reduces healthcare costs.

The aim of this study was to determine whether home telehealth, when integrated with the health facility's electronic medical record system, reduces healthcare costs and improves quality-of-life outcomes relative to usual home healthcare services for elderly high resource users with complex co-morbidities. Study patients were identified through the medical center's database. Intervention patients received home telehealth units that used standard phone lines to communicate with the hospital. FDA-approved peripheral devices monitored vital signs and valid questionnaires were used to evaluate quality-of-life outcomes. Out-of-range data triggered electronic alerts to nurse case managers. (No live video or audio was incorporated in either direction.) Templated progress notes facilitated seamless data entry into the patient's electronic medical record. Participants (n = 104) with complex heart failure, chronic lung disease, and/or diabetes mellitus were randomly assigned to an intervention or control group for 6-12 months. Parametric and nonparametric analyses were performed to compare outcomes for (1) subjective and objective quality-of-life measures, (2) health resource use, and (3) costs. In contrast to the control group, scores for home telehealth subjects showed a statistically significant decrease at 6 months for bed-days-of-care (p < 0.0001), urgent clinic/emergency room visits (p = 0.023), and A1C levels (p < 0.0001); at 12 months for cognitive status (p < 0.028); and at 3 months for patient satisfaction (p < 0.001). Functional levels and patient-rated health status did not show a significant difference for either group. Integrating home telehealth with the healthcare institution's electronic database significantly reduces resource use and improves cognitive status, treatment compliance, and stability of chronic disease for homebound elderly with common complex co-morbidities.