Predictive modeling of initiation and delayed mental health contact for depression

BACKGROUND

Depression is prevalent among Operation Enduring Freedom and Operation Iraqi Freedom (OEF/OIF) Veterans, yet rates of Veteran mental health care utilization remain modest. The current study examined: factors in electronic health records (EHR) associated with lack of treatment initiation and treatment delay; the accuracy of regression and machine learning models to predict initiation of treatment.

METHODS

We obtained data from the VA Corporate Data Warehouse (CDW). EHR data were extracted for 127,423 Veterans who deployed to Iraq/Afghanistan after 9/11 with a positive depression screen and a first depression diagnosis between 2001 and 2021. We also obtained 12-month pre-diagnosis and post-diagnosis patient data. Retrospective cohort analysis was employed to test if predictors can reliably differentiate patients who initiated, delayed, or received no mental health treatment associated with their depression diagnosis.

RESULTS

108,457 Veterans with depression, initiated depression-related care (55,492 Veterans delayed treatment beyond one month). Those who were male, without VA disability benefits, with a mild depression diagnosis, and had a history of psychotherapy were less likely to initiate treatment. Among those who initiated care, those with single and mild depression episodes at baseline, with either PTSD or who lacked comorbidities were more likely to delay treatment for depression. A history of mental health treatment, of an anxiety disorder, and a positive depression screen were each related to faster treatment initiation. Classification of patients was modest (ROC AUC = 0.59 95%CI = 0.586-0.602; machine learning F-measure = 0.46).

CONCLUSIONS

Having VA disability benefits was the strongest predictor of treatment initiation after a depression diagnosis and a history of mental health treatment was the strongest predictor of delayed initiation of treatment. The complexity of the relationship between VA benefits and history of mental health care with treatment initiation after a depression diagnosis is further discussed. Modest classification accuracy with currently known predictors suggests the need to identify additional predictors of successful depression management.

Mental health treatment utilization patterns among 108,457 Afghanistan and Iraq veterans with depression

People with depression often underutilize mental health care. This study was conceived as a first step toward a clinical decision support tool that helps identify patients who are at higher risk of underutilizing care. The primary goals were to (a) describe treatment utilization patterns, early termination, and return to care; (b) identify factors associated with early termination of treatment; and (c) evaluate the accuracy of regression models to predict early termination. These goals were evaluated in a retrospective cohort analysis of 108,457 U.S. veterans who received care from the Veterans Health Administration between 2001 and 2021. Our final sample was 16.5% female with an average age of 34.5. Veterans were included if they had a depression diagnosis, a positive depression screen, and received general health care services at least a year before and after their depression diagnosis. Using treatment quality guidelines, the threshold for treatment underutilization was defined as receiving fewer than four psychotherapy sessions or less than 84 days of antidepressants. Over one fifth of veterans (21.6%) received less than the minimally recommended care for depression. The odds of underutilizing treatment increased with lack of Veterans Administration benefits, male gender, racial/ethnic minority status, and having received mental health treatment in the past (adjusted OR > 1.1). Posttraumatic stress disorder comorbidity correlated with increased depression treatment utilization (adjusted OR < .9). Models with demographic and clinical information from medical records performed modestly in classifying patients who underutilized depression treatment (area under the curve = 0.595, 95% CI [0.588, 0.603]). Most veterans in this cohort received at least the minimum recommended treatment for depression. To improve the prediction of underutilization, patient factors associated with treatment underutilization likely need to be supplemented by additional clinical information. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Machine learning to develop a predictive model of pressure injury in persons with spinal cord injury

STUDY DESIGN

A 5-year longitudinal, retrospective, cohort study.

OBJECTIVES

Develop a prediction model based on electronic health record (EHR) data to identify veterans with spinal cord injury/diseases (SCI/D) at highest risk for new pressure injuries (PIs).

SETTING

Structured (coded) and text EHR data, for veterans with SCI/D treated in a VHA SCI/D Center between October 1, 2008, and September 30, 2013.

METHODS

A total of 4709 veterans were available for analysis after randomly selecting 175 to act as a validation (gold standard) sample. Machine learning models were created using ten-fold cross validation and three techniques: (1) two-step logistic regression; (2) regression model employing adaptive LASSO; (3) and gradient boosting. Models based on each method were compared using area under the receiver-operating curve (AUC) analysis.

RESULTS

The AUC value for the gradient boosting model was 0.62 (95% CI = 0.54-0.70), for the logistic regression model it was 0.67 (95% CI = 0.59-0.75), and for the adaptive LASSO model it was 0.72 (95% CI = 0.65-80). Based on these results, the adaptive LASSO model was chosen for interpretation. The strongest predictors of new PI cases were having fewer total days in the hospital in the year before the annual exam, higher vs. lower weight and most severe vs. less severe grade of injury based on the American Spinal Cord Injury Association (ASIA) Impairment Scale.

CONCLUSIONS

While the analyses resulted in a potentially useful predictive model, clinical implications were limited because modifiable risk factors were absent in the models.

Complementary and Integrative Health Approaches and Pain Care Quality in the Veterans Health Administration Primary Care Setting: A Quasi-Experimental Analysis

Background: Complementary and integrative health (CIH) approaches have been recommended in national and international clinical guidelines for chronic pain management. We set out to determine whether exposure to CIH approaches is associated with pain care quality (PCQ) in the Veterans Health Administration (VHA) primary care setting. Methods: We followed a cohort of 62,721 Veterans with newly diagnosed musculoskeletal disorders between October 2016 and September 2017 over 1-year. PCQ scores were derived from primary care progress notes using natural language processing. CIH exposure was defined as documentation of acupuncture, chiropractic or massage therapies by providers. Propensity scores (PSs) were used to match one control for each Veteran with CIH exposure. Generalized estimating equations were used to examine associations between CIH exposure and PCQ scores, accounting for potential selection and confounding bias. Results: CIH was documented for 14,114 (22.5%) Veterans over 16,015 primary care clinic visits during the follow-up period. The CIH exposure group and the 1:1 PS-matched control group achieved superior balance on all measured baseline covariates, with standardized differences ranging from 0.000 to 0.045. CIH exposure was associated with an adjusted rate ratio (aRR) of 1.147 (95% confidence interval [CI]: 1.142, 1.151) on PCQ total score (mean: 8.36). Sensitivity analyses using an alternative PCQ scoring algorithm (aRR: 1.155; 95% CI: 1.150-1.160) and redefining CIH exposure by chiropractic alone (aRR: 1.118; 95% CI: 1.110-1.126) derived consistent results. Discussion: Our data suggest that incorporating CIH approaches may reflect higher overall quality of care for patients with musculoskeletal pain seen in primary care settings, supporting VHA initiatives and the Declaration of Astana to build comprehensive, sustainable primary care capacity for pain management. Future investigation is warranted to better understand whether and to what degree the observed association may reflect the therapeutic benefits patients actually received or other factors such as empowering provider-patient education and communication about these approaches.

Extracting Pain Care Quality Indicators from U.S. Veterans Health Administration Chiropractic Care Using Natural Language Processing

BACKGROUND

Musculoskeletal pain is common in the Veterans Health Administration (VHA), and there is growing national use of chiropractic services within the VHA. Rapid expansion requires scalable and autonomous solutions, such as natural language processing (NLP), to monitor care quality. Previous work has defined indicators of pain care quality that represent essential elements of guideline-concordant, comprehensive pain assessment, treatment planning, and reassessment.

OBJECTIVE

Our purpose was to identify pain care quality indicators and assess patterns across different clinic visit types using NLP on VHA chiropractic clinic documentation.

METHODS

Notes from ambulatory or in-hospital chiropractic care visits from October 1, 2018 to September 30, 2019 for patients in the Women Veterans Cohort Study were included in the corpus, with visits identified as consultation visits and/or evaluation and management (E&M) visits. Descriptive statistics of pain care quality indicator classes were calculated and compared across visit types.

RESULTS

There were 11,752 patients who received any chiropractic care during FY2019, with 63,812 notes included in the corpus. Consultation notes had more than twice the total number of annotations per note (87.9) as follow-up visit notes (34.7). The mean number of total classes documented per note across the entire corpus was 9.4 (standard deviation [SD]  =  1.5). More total indicator classes were documented during consultation visits with (mean  =  14.8, SD  =  0.9) or without E&M (mean  =  13.9, SD  =  1.2) compared to follow-up visits with (mean  =  9.1, SD  =  1.4) or without E&M (mean  =  8.6, SD  =  1.5). Co-occurrence of pain care quality indicators describing pain assessment was high.

CONCLUSION

VHA chiropractors frequently document pain care quality indicators, identifiable using NLP, with variability across different visit types.

Mental Health Diagnoses are Not Associated With Indicators of Lower Quality Pain Care in Electronic Health Records of a National Sample of Veterans Treated in Veterans Health Administration Primary Care Settings

Prior research has demonstrated disparities in general medical care for patients with mental health conditions, but little is known about disparities in pain care. The objective of this retrospective cohort study was to determine whether mental health conditions are associated with indicators of pain care quality (PCQ) as documented by primary care clinicians in the Veterans Health Administration (VHA). We used natural language processing to analyze electronic health record data from a national sample of Veterans with moderate to severe musculoskeletal pain during primary care visits in the Fiscal Year 2017. Twelve PCQ indicators were annotated from clinician progress notes as present or absent; PCQ score was defined as the sum of these indicators. Generalized estimating equation Poisson models examined associations among mental health diagnosis categories and PCQ scores. The overall mean PCQ score across 135,408 person-visits was 8.4 (SD = 2.3). In the final adjusted model, post-traumatic stress disorder was associated with higher PCQ scores (RR = 1.006, 95%CI 1.002-1.010, P = .007). Depression, alcohol use disorder, other substance use disorder, schizophrenia, and bipolar disorder diagnoses were not associated with PCQ scores. Overall, results suggest that in this patient population, presence of a mental health condition is not associated with lower quality pain care. PERSPECTIVE: This study used a natural language processing approach to analyze medical records to determine whether mental health conditions are associated with indicators of pain care quality as documented by primary care clinicians. Findings suggest that presence of a diagnosed mental health condition is not associated with lower quality pain care.

Comparing Outcomes of the Veterans Health Administration's Traumatic Brain Injury and Mental Health Screening Programs: Types and Frequency of Specialty Services Used

The Veterans Health Administration (VHA) screens veterans who deployed in support of the wars in Afghanistan and Iraq for traumatic brain injury (TBI) and mental health (MH) disorders. Chronic symptoms after mild TBI overlap with MH symptoms, for which there are already established screens within the VHA. It is unclear whether the TBI screen facilitates treatment for appropriate specialty care over and beyond the MH screens. Our primary objective was to determine whether TBI screening is associated with different types (MH, Physical Medicine & Rehabilitation [PM&R], and Neurology) and frequency of specialty services compared with the MH screens. A retrospective cohort design examined veterans receiving VHA care who were screened for both TBI and MH disorders between Fiscal Year (FY) 2007 and FY 2018 (N = 241,136). We calculated service utilization counts in MH, PM&R, and Neurology in the six months after the screens. Zero-inflated negative binomial regression models of encounters (counts) were fit separately by specialty care type and for a total count of specialty services. We found that screening positive for TBI resulted in 2.38 times more specialty service encounters than screening negative for TBI. Compared with screening positive for MH only, screening positive for both MH and TBI resulted in 1.78 times more specialty service encounters and 1.33 times more MH encounters. The TBI screen appears to increase use of MH, PM&R, and Neurology services for veterans with post-deployment health concerns, even in those also identified as having a possible MH disorder.

Measuring pain care quality in the Veterans Health Administration primary care setting

ABSTRACT

The lack of a reliable approach to assess quality of pain care hinders quality improvement initiatives. Rule-based natural language processing algorithms were used to extract pain care quality (PCQ) indicators from documents of Veterans Health Administration primary care providers for veterans diagnosed within the past year with musculoskeletal disorders with moderate-to-severe pain intensity across 2 time periods 2013 to 2014 (fiscal year [FY] 2013) and 2017 to 2018 (FY 2017). Patterns of documentation of PCQ indicators for 64,444 veterans and 124,408 unique visits (FY 2013) and 63,427 veterans and 146,507 visits (FY 2017) are described. The most commonly documented PCQ indicators in each cohort were presence of pain, etiology or source, and site of pain (greater than 90% of progress notes), while least commonly documented were sensation, what makes pain better or worse, and pain's impact on function (documented in fewer than 50%). A PCQ indicator score (maximum = 12) was calculated for each visit in FY 2013 (mean = 7.8, SD = 1.9) and FY 2017 (mean = 8.3, SD = 2.3) by adding one point for every indicator documented. Standardized Cronbach alpha for total PCQ scores was 0.74 in the most recent data (FY 2017). The mean PCQ indicator scores across patient characteristics and types of healthcare facilities were highly stable. Estimates of the frequency of documentation of PCQ indicators have face validity and encourage further evaluation of the reliability, validity, and utility of the measure. A reliable measure of PCQ fills an important scientific knowledge and practice gap.

The Value of Extracting Clinician-Recorded Affect for Advancing Clinical Research on Depression: Proof-of-Concept Study Applying Natural Language Processing to Electronic Health Records

Background

Affective characteristics are associated with depression severity, course, and prognosis. Patients’ affect captured by clinicians during sessions may provide a rich source of information that more naturally aligns with the depression course and patient-desired depression outcomes.

Objective

In this paper, we propose an information extraction vocabulary used to pilot the feasibility and reliability of identifying clinician-recorded patient affective states in clinical notes from electronic health records.

Methods

Affect and mood were annotated in 147 clinical notes of 109 patients by 2 independent coders across 3 pilots. Intercoder discrepancies were settled by a third coder. This reference annotation set was used to test a proof-of-concept natural language processing (NLP) system using a named entity recognition approach.

Results

Concepts were frequently addressed in templated format and free text in clinical notes. Annotated data demonstrated that affective characteristics were identified in 87.8% (129/147) of the notes, while mood was identified in 97.3% (143/147) of the notes. The intercoder reliability was consistently good across the pilots (interannotator agreement [IAA] >70%). The final NLP system showed good reliability with the final reference annotation set (mood IAA=85.8%; affect IAA=80.9%).

Conclusions

Affect and mood can be reliably identified in clinician reports and are good targets for NLP. We discuss several next steps to expand on this proof of concept and the value of this research for depression clinical research.

Assessing the impact of the COVID-19 pandemic on pragmatic clinical trial participants

Characterizing the impacts of disruption attributable to the COVID-19 pandemic on clinical research is important, especially in pain research where psychological, social, and economic stressors attributable to the COVID-19 pandemic may greatly impact treatment effects. The National Institutes of Health - Department of Defense - Department of Veterans Affairs Pain Management Collaboratory (PMC) is a collective effort supporting 11 pragmatic clinical trials studying nonpharmacological approaches and innovative integrated care models for pain management in veteran and military health systems. The PMC rapidly developed a brief pandemic impacts measure for use across its pragmatic trials studying pain while remaining broadly applicable to other areas of clinical research. Through open discussion and consensus building by the PMC's Phenotypes and Outcomes Work Group, the PMC Coronavirus Pandemic (COVID-19) Measure was iteratively developed. The measure assesses the following domains (one item/domain): access to healthcare, social support, finances, ability to meet basic needs, and mental or emotional health. Two additional items assess infection status (personal and household) and hospitalization. The measure uses structured responses with a three-point scale for COVID-19 infection status and four-point ordinal rank response for all other domains. We recommend individualized adaptation as appropriate by clinical research teams using this measure to survey the effects of the COVID-19 pandemic on study participants. This can also help maintain utility of the measure beyond the COVID-19 pandemic to characterize impacts during future public health emergencies that may require mitigation strategies such as periods of quarantine and isolation.

Optimizing the Impact of Pragmatic Clinical Trials for Veteran and Military Populations: Lessons From the Pain Management Collaboratory

Pragmatic clinical trials (PCTs) are well-suited to address unmet healthcare needs, such as those arising from the dual public health crises of chronic pain and opioid misuse, recently exacerbated by the COVID-19 pandemic. These overlapping epidemics have complex, multifactorial etiologies, and PCTs can be used to investigate the effectiveness of integrated therapies that are currently available but underused. Yet individual pragmatic studies can be limited in their reach because of existing structural and cultural barriers to dissemination and implementation. The National Institutes of Health, Department of Defense, and Department of Veterans Affairs formed an interagency research partnership, the Pain Management Collaboratory. The partnership combines pragmatic trial design with collaborative tools and relationship building within a large network to advance the science and impact of nonpharmacological approaches and integrated models of care for the management of pain and common co-occurring conditions. The Pain Management Collaboratory team supports 11 large-scale, multisite PCTs in veteran and military health systems with a focus on team science with the shared aim that the "whole is greater than the sum of the parts." Herein, we describe this integrated approach and lessons learned, including incentivizing all parties; proactively offering frequent opportunities for problem-solving; engaging stakeholders during all stages of research; and navigating competing research priorities. We also articulate several specific strategies and their practical implications for advancing pain management in active clinical, "real-world," settings.

Examining the Relationship Between Environmental Factors and Inpatient Hospital Falls: Protocol for a Mixed Methods Study

BACKGROUND

Patient falls are the most common adverse events reported in hospitals. Although it is well understood that the physical hospital environment contributes to nearly 40% of severe or fatal hospital falls, there are significant gaps in the knowledge about the relationship between inpatient unit design and fall rates. The few studies that have examined unit design have been conducted in a single hospital (non-Veterans Health Administration [VHA]) or a small number of inpatient units, limiting generalizability. The goal of this study is to identify unit design factors contributing to inpatient falls in the VHA.

OBJECTIVE

The first aim of the study is to investigate frontline and management perceptions of and experiences with veteran falls as they pertain to inpatient environmental factors. An iterative rapid assessment process will be used to analyze the data. Interview findings will directly inform the development of an environmental assessment survey to be conducted as part of aim 2 and to contribute to interpretation of aim 2. The second aim of this study is to quantify unit design factors and compare spatial and environmental factors of units with higher- versus lower-than-expected fall rates.

METHODS

We will first conduct walk-through interviews with facility personnel in 10 medical/surgical units at 3 VHA medical centers to identify environmental fall risk factors. Data will be used to finalize an environmental assessment survey for nurse managers and facilities managers. We will then use fall data from the VA Inpatient Evaluation Center and patient data from additional sources to identify 50 medical/surgical nursing units with higher- and lower-than-expected fall rates. We will measure spatial factors by analyzing computer-aided design files of unit floorplans and environmental factors from the environmental assessment survey. Statistical tests will be performed to identify design factors that distinguish high and low outliers.

RESULTS

The VA Health Services Research and Development Service approved funding for the study. The research protocol was approved by institutional review boards and VA research committees at both sites. Data collection started in February 2018. Results of the data analysis are expected by February 2022. Data collection and analysis was completed for aim 1 with a manuscript of results in progress. For aim 2, the medical/surgical units were categorized into higher- and lower-than-expected fall categories, the environmental assessment surveys were distributed to facility managers and nurse managers. Data to measure spatial characteristics are being compiled.

CONCLUSIONS

To our knowledge, this study is the first to objectively identify spatial risks for falls in hospitals within in a large multihospital system. Findings can contribute to evidence-based design guidelines for hospitals such as those of the Facility Guidelines Institute and the Department of Veterans Affairs. The metrics for characterizing spatial features are quantitative indices that could be incorporated in larger scale contextual studies examining contributors to falls, which to date often exclude physical environmental factors at the unit level. Space syntax measures could be used as physical environmental factors in future research examining a range of contextual factors-social, personal, organizational, and environmental-that contribute to patient falls.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/24974.

Serious Falls in Middle-Aged Veterans: Development and Validation of a Predictive Risk Model

BACKGROUND/OBJECTIVES

Due to high rates of multimorbidity, polypharmacy, and hazardous alcohol and opioid use, middle-aged Veterans are at risk for serious falls (those prompting a visit with a healthcare provider), posing significant risk to their forthcoming geriatric health and quality of life. We developed and validated a predictive model of the 6-month risk of serious falls among middle-aged Veterans.

DESIGN

Cohort study.

SETTING

Veterans Health Administration (VA).

PARTICIPANTS

Veterans, aged 45 to 65 years, who presented for care within the VA between 2012 and 2015 (N = 275,940).

EXPOSURES

The exposures of primary interest were substance use (including alcohol and prescription opioid use), multimorbidity, and polypharmacy. Hazardous alcohol use was defined as an Alcohol Use Disorders Identification Test - Consumption (AUDIT-C) score of 3 or greater for women and 4 or greater for men. We used International Classification of Diseases, Ninth Revision (ICD-9), codes to identify alcohol and illicit substance use disorders and identified prescription opioid use from pharmacy fill-refill data. We included counts of chronic medications and of physical and mental health comorbidities.

MEASUREMENTS

We identified serious falls using external cause of injury codes and a machine-learning algorithm that identified serious falls in radiology reports. We used multivariable logistic regression with general estimating equations to calculate risk. We used an integrated predictiveness curve to identify intervention thresholds.

RESULTS

Most of our sample (54%) was aged 60 years or younger. Duration of follow-up was up to 4 years. Veterans who fell were more likely to be female (11% vs 7%) and White (72% vs 68%). They experienced 43,641 serious falls during follow-up. We identified 16 key predictors of serious falls and five interaction terms. Model performance was enhanced by addition of opioid use, as evidenced by overall category-free net reclassification improvement of 0.32 (P < .001). Discrimination (C-statistic = 0.76) and calibration were excellent for both development and validation data sets.

CONCLUSION

We developed and internally validated a model to predict 6-month risk of serious falls among middle-aged Veterans with excellent discrimination and calibration.

Evaluation of Complementary and Integrative Health Approaches Among US Veterans with Musculoskeletal Pain Using Propensity Score Methods

Objectives

To examine the treatment effectiveness of complementary and integrative health approaches (CIH) on chronic pain using Propensity Score (PS) methods.

Design, Settings, and Participants

A retrospective cohort of 309,277 veterans with chronic musculoskeletal pain assessed over three years after initial diagnosis.

Methods

CIH exposure was defined as one or more clinical visits for massage, acupuncture, or chiropractic care. The treatment effect of CIH on self-rated pain intensity was examined using a longitudinal model. PS-matching and inverse probability of treatment weighting (IPTW) were used to account for potential selection and confounding biases.

Results

At baseline, veterans with (7,621) and without (301,656) CIH exposure differed significantly in 21 out of 35 covariates. During the follow-up period, on average CIH recipients had 0.83 (95% confidence interval [CI] = 0.77 to 0.89) points higher pain intensity ratings (range = 0-10) than nonrecipients. This apparent unfavorable effect size was reduced to 0.37 (95% CI = 0.28 to 0.45) after PS matching, 0.36 (95% CI = 0.29 to 0.44) with IPTW on the treated (IPTW-T) weighting, and diminished to null when integrating IPTW-T with PS matching (0.004, 95% CI = -0.09 to 0.10). An alternative IPTW model and conventional covariate adjustment appeared least powerful in terms of potential bias reduction. Sensitivity analyses restricting the follow-up period to one year after CIH initiation derived consistent results.

Conclusions

PS-based causal methods successfully eliminated baseline difference between exposure groups in all measured covariates, yet they did not detect a significant difference in the self-rated pain intensity outcome between veterans who received CIHs and those who did not during the follow-up period.

Factors Associated With Wandering Behaviors in Veterans With Mild Dementia: A Prospective Longitudinal Community-Based Study

OBJECTIVE

To investigate baseline factors associated with caregiver-reported wandering among community-dwelling veterans with mild dementia.

METHODS

Veterans with mild dementia (N = 143) and their caregivers participated in a 2-year prospective longitudinal study. Measures assessed wandering, daily function, behavior, cognition, and personality features. Wandering was dichotomized as present or absent across study periods, and associations with baseline characteristics were examined.

RESULTS

One-quarter of participants demonstrated caregiver-reported wandering at 1 or more study visits, with 14% to 15% wandering at any 1 visit. Wandering was associated with significantly lower baseline scores in performance of daily function, behavioral response to stress, gait, and balance, and conscientiousness.

CONCLUSIONS

This novel study evaluated wandering in a community-dwelling sample of veterans with mild dementia. Wandering was associated with a specific personality trait, poorer behavioral response to stress as well as greater functional and gait/balance impairment. These findings may assist in developing community-based interventions for caregivers.

Classifying clinical notes with pain assessment using machine learning

Pain is a significant public health problem, affecting millions of people in the USA. Evidence has highlighted that patients with chronic pain often suffer from deficits in pain care quality (PCQ) including pain assessment, treatment, and reassessment. Currently, there is no intelligent and reliable approach to identify PCQ indicators inelectronic health records (EHR). Hereby, we used unstructured text narratives in the EHR to derive pain assessment in clinical notes for patients with chronic pain. Our dataset includes patients with documented pain intensity rating ratings > = 4 and initial musculoskeletal diagnoses (MSD) captured by (ICD-9-CM codes) in fiscal year 2011 and a minimal 1 year of follow-up (follow-up period is 3-yr maximum); with complete data on key demographic variables. A total of 92 patients with 1058 notes was used. First, we manually annotated qualifiers and descriptors of pain assessment using the annotation schema that we previously developed. Second, we developed a reliable classifier for indicators of pain assessment in clinical note. Based on our annotation schema, we found variations in documenting the subclasses of pain assessment. In positive notes, providers mostly documented assessment of pain site (67%) and intensity of pain (57%), followed by persistence (32%). In only 27% of positive notes, did providers document a presumed etiology for the pain complaint or diagnosis. Documentation of patients' reports of factors that aggravate pain was only present in 11% of positive notes. Random forest classifier achieved the best performance labeling clinical notes with pain assessment information, compared to other classifiers; 94, 95, 94, and 94% was observed in terms of accuracy, PPV, F1-score, and AUC, respectively. Despite the wide spectrum of research that utilizes machine learning in many clinical applications, none explored using these methods for pain assessment research. In addition, previous studies using large datasets to detect and analyze characteristics of patients with various types of pain have relied exclusively on billing and coded data as the main source of information. This study, in contrast, harnessed unstructured narrative text data from the EHR to detect pain assessment clinical notes. We developed a Random forest classifier to identify clinical notes with pain assessment information. Compared to other classifiers, ours achieved the best results in most of the reported metrics. Graphical abstract Framework for detecting pain assessment in clinical notes.

Leveraging Electronic Health Care Record Information to Measure Pressure Ulcer Risk in Veterans With Spinal Cord Injury: A Longitudinal Study Protocol

Background

Pressure ulcers (PrUs) are a frequent, serious, and costly complication for veterans with spinal cord injury (SCI). The health care team should periodically identify PrU risk, although there is no tool in the literature that has been found to be reliable, valid, and sensitive enough to assess risk in this vulnerable population.

Objective

The immediate goal is to develop a risk assessment model that validly estimates the probability of developing a PrU. The long-term goal is to assist veterans with SCI and their providers in preventing PrUs through an automated system of risk assessment integrated into the veteran’s electronic health record (EHR).

Methods

This 5-year longitudinal, retrospective, cohort study targets 12,344 veterans with SCI who were cared for in the Veterans Health Administration (VHA) in fiscal year (FY) 2009 and had no record of a PrU in the prior 12 months. Potential risk factors identified in the literature were reviewed by an expert panel that prioritized factors and determined if these were found in structured data or unstructured form in narrative clinical notes for FY 2009-2013. These data are from the VHA enterprise Corporate Data Warehouse that is derived from the EHR structured (ie, coded in database/table) or narrative (ie, text in clinical notes) data for FY 2009-2013.

Results

This study is ongoing and final results are expected in 2017. Thus far, the expert panel reviewed the initial list of risk factors extracted from the literature; the panel recommended additions and omissions and provided insights about the format in which the documentation of the risk factors might exist in the EHR. This list was then iteratively refined through review and discussed with individual experts in the field. The cohort for the study was then identified, and all structured, unstructured, and semistructured data were extracted. Annotation schemas were developed, samples of documents were extracted, and annotations are ongoing. Operational definitions of structured data elements have been created and steps to create an analytic dataset are underway.

Conclusions

To our knowledge, this is the largest cohort employed to identify PrU risk factors in the United States. It also represents the first time natural language processing and statistical text mining will be used to expand the number of variables available for analysis. A major strength of this quantitative study is that all VHA SCI centers were included in the analysis, reducing potential for selection bias and providing increased power for complex statistical analyses. This longitudinal study will eventually result in a risk prediction tool to assess PrU risk that is reliable and valid, and that is sensitive to this vulnerable population.

An Evolving Ecosystem for Natural Language Processing in Department of Veterans Affairs

In an ideal clinical Natural Language Processing (NLP) ecosystem, researchers and developers would be able to collaborate with others, undertake validation of NLP systems, components, and related resources, and disseminate them. We captured requirements and formative evaluation data from the Veterans Affairs (VA) Clinical NLP Ecosystem stakeholders using semi-structured interviews and meeting discussions. We developed a coding rubric to code interviews. We assessed inter-coder reliability using percent agreement and the kappa statistic. We undertook 15 interviews and held two workshop discussions. The main areas of requirements related to; design and functionality, resources, and information. Stakeholders also confirmed the vision of the second generation of the Ecosystem and recommendations included; adding mechanisms to better understand terms, measuring collaboration to demonstrate value, and datasets/tools to navigate spelling errors with consumer language, among others. Stakeholders also recommended capability to: communicate with developers working on the next version of the VA electronic health record (VistA Evolution), provide a mechanism to automatically monitor download of tools and to automatically provide a summary of the downloads to Ecosystem contributors and funders. After three rounds of coding and discussion, we determined the percent agreement of two coders to be 97.2% and the kappa to be 0.7851. The vision of the VA Clinical NLP Ecosystem met stakeholder needs. Interviews and discussion provided key requirements that inform the design of the VA Clinical NLP Ecosystem.

Qualitative Inquiry Explores Health-Related Quality of Life of Female Veterans With Post-Traumatic Stress Disorder

As the number of female veterans increases, health care systems must be prepared to meet the individualized needs of this population. To date, published data on health-related quality of life (HRQOL) of veterans with post-traumatic stress disorder (PTSD) focus on quantitative data and primarily represent the male population. The purpose of this study was to qualitatively explore the impact of PTSD on female veterans' HRQOL. A descriptive qualitative study used focus groups and demographic surveys to achieve data collection in a sample of veterans with PTSD. This report focuses on the analysis of a sample of 12 females to explore PTSD HRQOL experiences unique to female veterans. Female veterans reported several areas in which their HRQOL was impacted adversely in social participation, physical, cognitive, and emotional aspects of their lives. Issues with self-medication and substance abuse were also reported by participants. Female participants' perceptions about Veterans Health Administration were also discussed, highlighting unmet needs when receiving care for PTSD. These data provide unique insights from the perspective of female veterans with PTSD about their HRQOL and receiving care within the Veterans Health Administration health care system. These data can inform future research to better address the needs of female veterans living with PTSD.

Risk assessment of wandering behavior in mild dementia

OBJECTIVE

This prospective longitudinal study aims to determine the risk factors of wandering-related adverse consequences in community-dwelling persons with mild dementia. These adverse consequences include negative outcomes of wandering (falls, fractures, and injuries) and eloping behavior.

METHODS

We recruited 143 dyads of persons with mild dementia and their caregivers from a veteran's hospital and memory clinic in Florida. Wandering-related adverse consequences were measured using the Revised Algase Wandering Scale - Community Version. Variables such as personality (Big Five Inventory), behavioral response to stress, gait, and balance (Tinetti Gait and Balance), wayfinding ability (Wayfinding Effectiveness Scale), and neurocognitive abilities (attention, cognition, memory, language/verbal skills, and executive functioning) were also measured. Bivariate and logistic regression analyses were performed to assess the predictors of these wandering-related adverse consequences.

RESULTS

A total of 49% of the study participants had falls, fractures, and injuries due to wandering behavior, and 43.7% demonstrated eloping behaviors. Persistent walking (OR = 2.6) and poor gait (OR = 0.9) were significant predictors of negative outcomes of wandering, while persistent walking (OR = 13.2) and passivity (OR = 2.55) predicted eloping behavior. However, there were no correlations between wandering-related adverse consequences and participants' characteristics (age, gender, race, ethnicity, and education), health status (Charlson comorbidity index), or neurocognitive abilities.

CONCLUSION

Our results highlight the importance of identifying at-risk individuals so that effective interventions can be developed to reduce or prevent the adverse consequences of wandering.

Improving identification of fall-related injuries in ambulatory care using statistical text mining

OBJECTIVES

We determined whether statistical text mining (STM) can identify fall-related injuries in electronic health record (EHR) documents and the impact on STM models of training on documents from a single or multiple facilities.

METHODS

We obtained fiscal year 2007 records for Veterans Health Administration (VHA) ambulatory care clinics in the southeastern United States and Puerto Rico, resulting in a total of 26 010 documents for 1652 veterans treated for fall-related injury and 1341 matched controls. We used the results of an STM model to predict fall-related injuries at the visit and patient levels and compared them with a reference standard based on chart review.

RESULTS

STM models based on training data from a single facility resulted in accuracy of 87.5% and 87.1%, F-measure of 87.0% and 90.9%, sensitivity of 92.1% and 94.1%, and specificity of 83.6% and 77.8% at the visit and patient levels, respectively. Results from training data from multiple facilities were almost identical.

CONCLUSIONS

STM has the potential to improve identification of fall-related injuries in the VHA, providing a model for wider application in the evolving national EHR system.

A Case Study of Data Quality in Text Mining Clinical Progress Notes

Text analytic methods are often aimed at extracting useful information from the vast array of unstructured, free format text documents that are created by almost all organizational processes. The success of any text mining application rests on the quality of the underlying data being analyzed, including both predictive features and outcome labels. In this case study, some focused experiments regarding data quality are used to assess the robustness of Statistical Text Mining (STM) algorithms when applied to clinical progress notes. In particular, the experiments consider the impacts of task complexity (by removing signals), training set size, and target outcome quality. While this research is conducted using a dataset drawn from the medical domain, the data quality issues explored are of more general interest.

Using information from the electronic health record to improve measurement of unemployment in service members and veterans with mTBI and post-deployment stress

OBJECTIVE

The purpose of this pilot study is 1) to develop an annotation schema and a training set of annotated notes to support the future development of a natural language processing (NLP) system to automatically extract employment information, and 2) to determine if information about employment status, goals and work-related challenges reported by service members and Veterans with mild traumatic brain injury (mTBI) and post-deployment stress can be identified in the Electronic Health Record (EHR).

DESIGN

Retrospective cohort study using data from selected progress notes stored in the EHR.

SETTING

Post-deployment Rehabilitation and Evaluation Program (PREP), an in-patient rehabilitation program for Veterans with TBI at the James A. Haley Veterans' Hospital in Tampa, Florida.

PARTICIPANTS

Service members and Veterans with TBI who participated in the PREP program (N = 60).

MAIN OUTCOME MEASURES

Documentation of employment status, goals, and work-related challenges reported by service members and recorded in the EHR.

RESULTS

Two hundred notes were examined and unique vocational information was found indicating a variety of self-reported employment challenges. Current employment status and future vocational goals along with information about cognitive, physical, and behavioral symptoms that may affect return-to-work were extracted from the EHR. The annotation schema developed for this study provides an excellent tool upon which NLP studies can be developed.

CONCLUSIONS

Information related to employment status and vocational history is stored in text notes in the EHR system. Information stored in text does not lend itself to easy extraction or summarization for research and rehabilitation planning purposes. Development of NLP systems to automatically extract text-based employment information provides data that may improve the understanding and measurement of employment in this important cohort.

TagLine: Information Extraction for Semi-Structured Text in Medical Progress Notes

Statistical text mining and natural language processing have been shown to be effective for extracting useful information from medical documents. However, neither technique is effective at extracting the information stored in semi-structure text elements. A prototype system (TagLine) was developed to extract information from the semi-structured text using machine learning and a rule based annotator. Features for the learning machine were suggested by prior work, and by examining text, and selecting attributes that help distinguish classes of text lines. Classes were derived empirically from text and guided by an ontology developed by the VHA's Consortium for Health Informatics Research (CHIR). Decision trees were evaluated for class predictions on 15,103 lines of text achieved an overall accuracy of 98.5 percent. The class labels applied to the lines were then used for annotating semi-structured text elements. TagLine achieved F-measure over 0.9 for each of the structures, which included tables, slots and fillers.

Validity and reliability of a pressure ulcer monitoring tool for persons with spinal cord impairment

OBJECTIVE

The purpose was to provide support for validity and reliability of the spinal cord impairment pressure ulcer monitoring tool (SCI-PUMT) to assess pressure ulcer (PrU) healing.

DESIGN

Expert panels developed a 30-item pool, including new items and items from two established PrU healing tools, to represent potential variables for monitoring PrU healing. Subjects were prospectively assessed weekly for each variable over a 12-week period.

SETTING

Data collection was conducted on a cohort of inpatients and outpatients in one Spinal Cord Injury/Disorders Center in the Veterans' Health Administration.

SUBJECTS

A convenience sample of Veterans (n = 66) with spinal cord impairment (SCI) was recruited. Eligible subjects had at least one PrU (n = 167) and a history of SCI for longer than 1 year. Interventions Not applicable.

OUTCOME MEASURE

A change in PrU volume was calculated using VeV Measurement Documentation software and a digital imaging camera.

RESULTS

Content validity was established for a pool of items designed to gauge PrU healing. Exploratory factor analysis (construct validity) identified a parsimonious set of seven items for inclusion in the SCI-PUMT to assess PrU healing. The SCI-PUMT was found to explain 59% of the variance of the volume across the study. Inter-rater reliability was 0.79 and intra-rater reliability ranged from 0.81 to 0.99 among research assistants. Similar levels of reliability were subsequently established among registered nurses, who used the SCI-PUMT in the clinical setting.

CONCLUSIONS

The final version of the SCI-PUMT was determined to be valid, reliable, and sensitive in detecting PrU healing over time in Veterans with SCI.

The use of breast-conserving surgery for women treated for breast cancer in the Department of Veterans Affairs

BACKGROUND

Previous non-stage-adjusted research described a lower use of breast-conserving surgery (BCS) for the treatment of breast cancer in the Veterans Health Administration (VHA) facilities than in the private sector.

METHODS

We combined data from the VHA Centralized Cancer Registry with administrative datasets to describe surgical treatment for locoregional breast cancer in VHA facilities from 2000 to 2006.

RESULTS

When considering only procedures performed in VHA facilities, BCS rates decreased from 50.5% (53/105) in 2000 to 42.3% (n = 58/137) in 2006; however, after accounting for procedures conducted in the private sector and paid for by the VHA, BCS rates approached those experienced in breast cancer patients cared for outside the VHA.

CONCLUSIONS

Based solely on procedures performed in the VHA, rates of BCS use are much lower in the VHA than in the private sector. We were able to show similar rates of BCS use when we accounted for procedures paid for by the VHA but performed at an outside facility. Further exploration and prospective analyses to examine these findings are needed.

Finding falls in ambulatory care clinical documents using statistical text mining

OBJECTIVE

To determine how well statistical text mining (STM) models can identify falls within clinical text associated with an ambulatory encounter.

MATERIALS AND METHODS

2241 patients were selected with a fall-related ICD-9-CM E-code or matched injury diagnosis code while being treated as an outpatient at one of four sites within the Veterans Health Administration. All clinical documents within a 48-h window of the recorded E-code or injury diagnosis code for each patient were obtained (n=26 010; 611 distinct document titles) and annotated for falls. Logistic regression, support vector machine, and cost-sensitive support vector machine (SVM-cost) models were trained on a stratified sample of 70% of documents from one location (dataset Atrain) and then applied to the remaining unseen documents (datasets Atest-D).

RESULTS

All three STM models obtained area under the receiver operating characteristic curve (AUC) scores above 0.950 on the four test datasets (Atest-D). The SVM-cost model obtained the highest AUC scores, ranging from 0.953 to 0.978. The SVM-cost model also achieved F-measure values ranging from 0.745 to 0.853, sensitivity from 0.890 to 0.931, and specificity from 0.877 to 0.944.

DISCUSSION

The STM models performed well across a large heterogeneous collection of document titles. In addition, the models also generalized across other sites, including a traditionally bilingual site that had distinctly different grammatical patterns.

CONCLUSIONS

The results of this study suggest STM-based models have the potential to improve surveillance of falls. Furthermore, the encouraging evidence shown here that STM is a robust technique for mining clinical documents bodes well for other surveillance-related topics.

Using ensemble models to classify the sentiment expressed in suicide notes

In 2007, suicide was the tenth leading cause of death in the U.S. Given the significance of this problem, suicide was the focus of the 2011 Informatics for Integrating Biology and the Bedside (i2b2) Natural Language Processing (NLP) shared task competition (track two). Specifically, the challenge concentrated on sentiment analysis, predicting the presence or absence of 15 emotions (labels) simultaneously in a collection of suicide notes spanning over 70 years. Our team explored multiple approaches combining regular expression-based rules, statistical text mining (STM), and an approach that applies weights to text while accounting for multiple labels. Our best submission used an ensemble of both rules and STM models to achieve a micro-averaged F₁ score of 0.5023, slightly above the mean from the 26 teams that competed (0.4875).

Using ontology network structure in text mining

Statistical text mining treats documents as bags of words, with a focus on term frequencies within documents and across document collections. Unlike natural language processing (NLP) techniques that rely on an engineered vocabulary or a full-featured ontology, statistical approaches do not make use of domain-specific knowledge. The freedom from biases can be an advantage, but at the cost of ignoring potentially valuable knowledge. The approach proposed here investigates a hybrid strategy based on computing graph measures of term importance over an entire ontology and injecting the measures into the statistical text mining process. As a starting point, we adapt existing search engine algorithms such as PageRank and HITS to determine term importance within an ontology graph. The graph-theoretic approach is evaluated using a smoking data set from the i2b2 National Center for Biomedical Computing, cast as a simple binary classification task for categorizing smoking-related documents, demonstrating consistent improvements in accuracy.

A cardiovascular profile score in the surveillance of fetal hydrops

OBJECTIVE

To assess the value of a cardiovascular profile score in the surveillance of fetal hydrops.

METHODS

In a retrospective study, 102 hydropic fetuses were examined between 15 and 37 completed weeks of gestation with ultrasonographic assessment of hydrops, heart size, and cardiac function, and arterial umbilical and venous Doppler sonography of the ductus venosus (DV) and the umbilical vein (UV). A cardiovascular profile score (CVPS) was constructed by attributing 2 points for normal and taking away 1 or 2 points for abnormal findings in each category. The score of the final examination prior to treatment, delivery, or fetal demise was compared to the fetal outcome in these 102 fetuses after exclusion of terminated pregnancies. The scores of the first and last examinations were compared in 40 fetuses and the relationship between these scores and the evolution of fetal hydrops and fetal outcome was assessed.

RESULTS

Twenty-one pregnancies were terminated (21%). Fifty-four of the remaining 81 hydropic fetuses survived (67%) and perinatal death (PNM) occurred in 27 fetuses (33%). The median CVPS was 6.0 (IQR 4.75-8.00) for all fetuses, with a median of 6.0 (IQR 5.00-6.00) in fetuses who died in the perinatal period compared to a median of 7.0 (IQR 4.00-8.00) in those who survived (p < 0.035). All fetuses in this study had a 'severe' form of hydrops with skin edema. The best predictor for adverse outcome was the venous Doppler sonography of UV and DV, in particular umbilical venous pulsations. Among fetuses included in the longitudinal arm of the study, the survival rate was 40% and the PNM was 60%, after exclusion of terminated pregnancies. CVPS increased by a median of 1 (IQR 0.00-2.00) point in the last exam for those fetuses that lived, whereas among those fetuses that died, the CVPS decreased by a median 1.5 (IQR 0.25-2.75) points (p < 0.001).

CONCLUSIONS

The fetal cardiovascular profile score can be used in the surveillance of hydropic fetuses for prediction of the presence of congestive heart failure and as an aid for predicting fetal outcome.

Using patient safety indicators to estimate the impact of potential adverse events on outcomes

The authors estimated the impact of potentially preventable patient safety events, identified by Agency for Healthcare Research and Quality (AHRQ) Patient Safety Indicators (PSIs), on patient outcomes: mortality, length of stay (LOS), and cost. The PSIs were applied to all acute inpatient hospitalizations at Veterans Health Administration (VA) facilities in fiscal 2001. Two methods-regression analysis and multivariable case matching- were used independently to control for patient and facility characteristics while predicting the effect of the PSI on each outcome. The authors found statistically significant (p < .0001) excess mortality, LOS, and cost in all groups with PSIs. The magnitude of the excess varied considerably across the PSIs. These VA findings are similar to those from a previously published study of nonfederal hospitals, despite differences between VA and non-VA systems. This study contributes to the literature measuring outcomes of medical errors and provides evidence that AHRQ PSIs may be useful indicators for comparison across delivery systems.

Nurse Staffing and Patient Outcomes in Inpatient Rehabilitation Settings

In rehabilitation nursing, the patient classification systems or acuity models and nurse-staffing ratios are not supported by empirical evidence. Moreover there are no studies published characterizing nursing hours per patient day, proportion of RN staff and impact of agency nurses in inpatient rehabilitation settings. The purpose of this prospective observational study was to describe rehabilitation nurse staffing patterns, to validate the impact of rehabilitation nursing on patient outcomes, and to test whether existing patient measures on severity and outcomes in rehabilitation could be used as a proxy for burden of care to predict rehabilitation nurse staffing ceilings and daily nurse staffing requirements. A total of 54 rehabilitation facilities in the United States, stratified by geography, were randomly selected to participate in the study.

Elimination of up to 80% of human pancreatic adenocarcinomas in athymic mice by cardiac hormones

BACKGROUND

Four cardiac hormones have anticancer effects in vitro: i) atrial natriuretic peptide (ANP), ii) vessel dilator, iii) long acting natriuretic peptide (LANP), and iv) kaliuretic peptide.

MATERIALS AND METHODS

These cardiac hormones were infused subcutaneously for 28 days with weekly fresh hormones at 3 nM min(-1) kg(-1) body weight in athymic mice bearing human pancreatic adenocarcinomas.

RESULTS

ANP, vessel dilator, LANP and kaliuretic peptide eliminated 80%, 33%, 20% and 14% of the pancreatic adenocarcinomas. Even in the treated animals which did not have a total cure, their tumor volume decreased to less than 10% (and with vessel dilator to 2%) of that of the untreated animals. The natriuretic peptide receptor (NPR)-A receptor was decreased 33% to 55% in the metastatic lesions compared to the primary pancreatic adenocarcinoma.

CONCLUSION

Four cardiac hormones eliminated up to 80% of human pancreatic adenocarcinomas in athymic mice.

A Pilot Study to Modify the SF-36V Physical Functioning Scale for Use With Veterans With Spinal Cord Injury

OBJECTIVE

To develop a valid and reliable spinal cord injury (SCI) specific physical functioning (PF) scale for the Veterans Health Administration (VHA) version of the 36-Item Short-Form Health Survey.

DESIGN

A mixed qualitative and quantitative research design was used. In phase 1, a pool of SCI-specific PF items was generated based on focus groups with patients and health care providers. In phase 2, the psychometric properties of the SCI-specific PF scale were established.

SETTING

A VHA SCI center.

PARTICIPANTS

The sample consisted of valid responses from 359 veterans with traumatic SCI who were seen at a VHA SCI center during the prior year (2002).

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURE

Physical functioning in people with SCI.

RESULTS

Exploratory factor analysis was conducted separately on respondents with lower neurologic-level injuries (paraplegia, 53% [n=190]) and those with higher neurologic-level injuries (tetraplegia, 45% [n=163]) and identified 9 items loading on 1 factor in both groups. These 9 items were included in separate item response theory (IRT) model analyses for each subgroup. Based on the IRT analysis, 1 item was eliminated, resulting in an 8-item, SCI-specific PF scale.

CONCLUSIONS

Although several of the items in the SCI-specific PF scale showed floor effects, particularly in people with tetraplegia, we found excellent reliability and strong support of convergent and divergent validity of the scale.

Drugs and falls in community-dwelling older people: a national veterans study

OBJECTIVE

The aim of this study was to identify which specific medications within recognized major problematic drug categories that increase risk of falling were prescribed to veterans before their out-patient treatment for a fall.

METHODS

This was a retrospective, cross-sectional national secondary outpatient data analysis with an age- and sex-matched comparison group. The setting was the national Veterans Health Administration (VHA) ambulatory health care system in fiscal year (FY) 2004. The study population was VHA patients aged>or=65 years who had fall-related outpatient clinical health care encounters in FY 2004 (as indicated by diagnostic codes) and who received >or=1 outpatient medication during the study period. The age- and sex-matched comparison group consisted of an equal number of patients with nonspecific chest pain. The percentage of patients in each group receiving medications (at the time of the outpatient encounter) that affect the cardiovascular system (CVS), central nervous system (CNS), or musculoskeletal system (MSS) was compared with Bonferrom-adjusted P values.

RESULTS

The study sample consisted of 20,551 patients; the comparison group included the same number of patients. More patients with fall-coded encounters used CNS drugs than those with nonspecific chest pain (42.05% vs 29.29%). Also, within the CNS category, more patients with fall-coded encounters used antiparkinsonian medications (3.67% vs 1.32%), Alzheimer's disease medications (ie, cholinesterase inhibitors [5.40% vs 2.35%]), anticonvulsants/barbiturates (8.95% vs 5.18%), antidepressants (22.50% vs 14.16%), antipsychotics (4.68% vs 2.01%), opioid analgesics and narcotics (11.21% vs 9.09%), and benzodiazepines (7.60% vs 5.96%) (all, P<0.002). More patients with nonspecific chest pain received CVS drugs compared with the fall-coded group (69.13% vs 63.07%; P<0.002). Within the CVS category, more patients in the nonspecific chest pain group received angiotensin-II receptor antagonists, angiotensin-converting enzyme inhibitors, beta-blockers, calcium channel blockers, vasodilators, diuretics, and antiarrhythmics (all, P<0.002). No differences were noted between groups in the MSS category, except for NSAIDs, which more patients in the nonspecific chest pain group used than in the fall-coded group (6.44% vs 5.63%; P<0.002).

CONCLUSION

In this study, subjects with a health care encounter for a fall (as indicated by diagnostic code) were prescribed significantly more CNS-category medications than subjects in the age- and sex-matched comparison group.

Using administrative data to track fall-related ambulatory care services in the Veterans Administration Healthcare system

BACKGROUND AND AIMS

The Veterans Administration (VA) Healthcare system, containing hospital and community-based outpatient clinics, provides the setting for the study. Summary data was obtained from the VA Ambulatory Events Database for fiscal years (FY) 1997-2001 and in-depth data for FY 2001. In FY 2001, the database included approximately 4 million unique patients with 60 million encounters. The purpose of this study was: 1) to quantify injuries and use of services associated with falls among the elderly treated in Veterans Administration (VA) ambulatory care settings using administrative data; 2) to compare fall-related services provided to elderly veterans with those provided to younger veterans.

METHODS

Retrospective analysis of administrative data. This study describes the trends (FY 1997-2001) and patterns of fall-related ambulatory care encounters (FY 2001) in the VA Healthcare System.

RESULTS

An approximately four-fold increase in both encounters and patients seen was observed in FY 1997-2001, largely paralleling the growth of VA ambulatory care services. More than two-thirds of the patients treated were found to be over the age of 65. Veterans over the age of 65 were found to be more likely to receive care in the non-urgent setting and had higher numbers of co-morbid conditions than younger veterans. While nearly half of the encounters occurred in the Emergency/Urgent Care setting, fall-related injuries led to services across a wide spectrum of medical and surgical providers/departments.

CONCLUSIONS

This study represents the first attempt to use the VA Ambulatory Events Database to study fall-related services provided to elderly veterans. In view of the aging population served by the VA and the movement to provide increased services in the outpatient setting, this database provides an important resource for researchers and administrators interested in the prevention and treatment of fall-related injuries.

Hispanic Health Status in Orange County, Florida

PURPOSE

To assess the health status of the Hispanic population of Orange County, Florida.

METHODS

The methodology utilized secondary data for 66 ethnically identified indicators in a comparative framework applied for a 5-year period (1997-2001).

FINDINGS

Orange County Hispanics are younger with lower per capita income than their Florida peers, less likely to be White, and much more likely to be of Puerto Rican origin. Relative to the Hispanic populations in the selected peer counties and statewide, Orange County Hispanics have higher age-adjusted death rates for a majority of disease categories and conditions, such as breast, lung, and prostate cancers; chronic liver disease and cirrhosis; diabetes mellitus; pneumonia and influenza; stroke; acquired immunodeficiency syndrome; motor vehicle accidents; and infant, neonatal, and child mortality. Orange County Hispanics did better in comparison to Orange non-Hispanics, with lower age-adjusted death rates for major causes of death such as heart disease, cancer, and stroke. However, for many indicators, the 5-year trends for Orange County Hispanics are moving in an unfavorable direction in contrast to the trends for non-Hispanics, which are either stable or improving.

CONCLUSION

Comparative assessments of Hispanic populations using secondary data enable the development of a comprehensive health status profile. However, this approach is currently constrained by the limited number of ethnically identified indicators and, especially for Hispanics, problems in the accuracy and consistency of the assignment to racial categories and subsequent reporting.

A comparison of patient outcomes and quality of life in persons with neurogenic bowel: standard bowel care program vs colostomy

BACKGROUND/OBJECTIVE

The purpose of this study was to compare patient outcomes and quality of life for people with neurogenic bowel using either a standard bowel care program or colostomy.

METHODS

We analyzed survey data from a national sample, comparing outcomes between veterans with spinal cord injury (SCI) who perform bowel care programs vs individuals with colostomies. This study is part of a larger study to evaluate clinical practice guideline implementation in SCI. The sample included 1,503 veterans with SCI. The response rate was 58.4%. For comparison, we matched the respondents with colostomies to matched controls from the remainder of the survey cohort. A total of 74 veterans with SCI and colostomies were matched with 296 controls, using propensity scores. Seven items were designed to elicit information about the respondent's satisfaction with their bowel care program, whereas 7 other items were designed to measure bowel-related quality of life.

RESULTS

No statistically significant differences in satisfaction or quality of life were found between the responses from veterans with colostomies and those with traditional bowel care programs. Both respondents with colostomies and those without colostomies indicated that they had received training for their bowel care program, that they experienced relatively few complications, such as falls as a result of their bowel care program, and that their quality of life related to bowel care was generally good. However, large numbers of respondents with colostomies (n = 39; 55.7%) and without colostomies (n = 113; 41.7%) reported that they were very unsatisfied with their bowel care program.

CONCLUSION

Satisfaction with bowel care is a major problem for veterans with SCI.

The application of volume-outcome contouring in data warehousing

Despite a compelling body of published research on the nature of provider volume and clinical outcomes, healthcare executives and policymakers have not managed to develop and implement systems that are useful in directing patients to higher volume providers via selective referral or avoidance. A specialized data warehouse application, utilizing hospital discharge data linked to physician biographical information, allows detailed analysis of physician and hospital volume and the resulting pattern (contour) of related outcomes such as mortality, complications, and medical errors. The approach utilizes a historical repository of hospital discharge data in which the outcomes of interest, important patient characteristics and risk factors used in severity-adjusting of the outcomes are derived from the coding structure of the data.

Provider attitudes and beliefs about clinical practice guidelines

The goals of clinical practice guidelines (CPG) are to improve the process and outcomes of health care, decrease practice variation, and optimize resource utilization. The objectives of this study were to (a) describe overall provider attitudes and beliefs about CPG, and (b) describe provider attitudes and acceptance of two specific spinal cord injury (SCI) CPG. A total of 152 health care providers responsible for implementation of the CPG at participating Veterans Health Administration (VHA) SCI sites responded to a survey (response rate of 35%). Overall, SCI care providers expressed positive attitudes towards CPG, including the two SCI guidelines included in this study. A comparison of responses revealed relatively few areas in which differences existed among SCI facilities and provider groups. Nurses represented the largest provider group participating in this survey and consistently expressed the most positive responses. In particular, nurses were more positive about guidelines, recognized the benefits of the guidelines, and were more willing to support the development of guidelines, compared to other providers in the study. The results of this study suggest that negative attitudes and beliefs about guidelines might be less of an obstacle to guideline implementation in VHA SCI Centers. Nurses are in a position to play a key role in their implementation.

A method to measure the impact of primary care programs targeted to reduce racial and ethnic disparities in health outcomes

A retrospective population-based study was designed to test the impact on selected health outcomes of community-based primary care programs targeting racial and ethnic minorities. Zip codes were coded as either "high" or "low" access to targeted primary care programs to create the independent variable of interest. Outcome measures were chosen to represent unique dimensions of primary care. Generalized linear models were developed to compare rates for the outcome measures among blacks in high- and low-access areas. This study provides a useful approach that could be used to evaluate the impact of such programs in other communities.

Data abstraction: designing the tools, recruiting and training the data abstractors

Data abstraction is very exacting work requiring that data abstractors have both knowledge and experience of phenomena being studied. Quality of the data and efficiency of the data abstractors are dependent upon the content and design of the data abstraction tools. Taking the time to recruit and train qualified data abstractors and to develop effective data abstraction tools will result in data that not only answers the research question, but also withstands the most rigorous critique.

Toward a population health delivery system: first steps in performance measurement

In spite of the technological sophistication and clinical excellence of the U.S. health care industry and annual health expenditures in excess of a trillion dollars, the overall health status of the American population is comparatively poor. The BCHS in west central Florida sought to improve the health status of the communities that it serves. Known by the acronym CHAPIR, an information-driven health status decision support system was developed, pilot tested, and is now fully implemented throughout the BCHS. The methodological approach, quantitative indicators, report format components, and management implications of the system are described.

Cost consequences of sentinel lymph node biopsy in the treatment of breast cancer. A preliminary analysis

OBJECTIVES

To assess whether sentinel lymph node biopsy (SLNB), an alternative to axillary lymph node dissection in treating female breast cancer, affords any cost savings.

METHODS

We profile cumulative treatment costs of 811 breast cancer patients, 555 of whom received SLNB. Univariate and multivariate statistical tests are used to appraise whether these cost profiles differ between SLNB and other patients.

RESULTS

The statistical results are mixed. However, none supports the conjecture that SLNB necessarily lowers the cost of treating the average breast patient.

CONCLUSIONS

SLNB may be cost-effective, but longer term costs and outcomes must be estimated before firm conclusions can be reached.

Physician practice volume and alternative surgical treatment for breast cancer in Florida

OBJECTIVE

To determine whether surgeon procedure volume is related to the selection of a surgical option (mastectomy versus breast-conserving surgery) for breast cancer treatment . STUDY SETTING/STUDY DESIGN: Secondary data sources were used to study surgical procedures performed for female breast cancer in Florida during the years 1997-98 in a retrospective population-based analysis.

DATA EXTRACTION

Surgical procedures for female breast cancer in Florida were identified during 1997 and 1998 (N = 28,380) by combining data from the Florida Acute Hospital and Short-term Psychiatric Inpatient Data Collection and the Ambulatory Outpatient Data Collection. A total of 1,320 physicians who provided breast surgical procedures in Florida during the two-year study period were identified.

PRINCIPAL FINDINGS

After controlling for selected patient and physician characteristics, the lowest volume surgeons were nearly twice as likely to perform mastectomies rather th an breast-conserving surgery compared with the highest volume group. Patients with Medicaid as an insurer were also nearly twice as likely to receive mastectomies. Patient demographic factors such as age, while statistically significant, were shown to be far less predictive of procedure choice. Forty-two percent of the physicians performed fewer than two surgeries on average per year.

CONCLUSIONS

Patients treated by lower volume physicians have a greater likelihood of receiving mastectomies than do those patients treated by higher volume physicians.