Online electronic data capture and research data repository system for clinical and translational research.

Data is at the core of any clinical and translational research (CTR). In many studies, the electronic data capture (EDC) method has been found to be more efficient than standard paper-based data collection methods in many aspects, including accuracy, integrity, timeliness, and cost-effectiveness. The objective of this article is to present a secure, web-based EDC system for CTR that has been implemented by the Institute for Clinical and Translational Science (iCATS) at the University of Missouri School of Medicine.

Secondary Use of Clinical Data to Enable Data-Driven Translational Science with Trustworthy Access Management.

University of Missouri (MU) Health Care produces a large amount of digitized clinical data that can be used in clinical and translational research for cohort identification, retrospective data analysis, feasibility study, and hypothesis generation. In this article, the implementation of an integrated clinical research data repository is discussed. We developed trustworthy access-management protocol for providing access to both clinically relevant data and protected health information. As of September 2014, the database contains approximately 400,000 patients and 82 million observations; and is growing daily. The system will facilitate the secondary use of electronic health record (EHR) data at MU to promote data-driven clinical and translational research, in turn enabling better healthcare through research.

Individual-Level and Neighborhood-Level Factors Associated with Longitudinal Changes in Cardiometabolic Measures in Participants of a Clinic-Based Care Coordination Program: A Secondary Data Analysis.

Background: Identifying individual and neighborhood-level factors associated with worsening cardiometabolic risks despite clinic-based care coordination may help identify candidates for supplementary team-based care. Methods: Secondary data analysis of data from a two-year nurse-led care coordination program cohort of Medicare, Medicaid, dual-eligible adults, Leveraging Information Technology to Guide High Tech, High Touch Care (LIGHT2), from ten Midwestern primary care clinics in the U.S. Outcome Measures: Hemoglobin A1C, low-density-lipoprotein (LDL) cholesterol, and blood pressure. Multivariable generalized linear regression models assessed individual and neighborhood-level factors associated with changes in outcome measures from before to after completion of the LIGHT2 program. Results: 6378 participants had pre-and post-intervention levels reported for at least one outcome measure. In adjusted models, higher pre-intervention cardiometabolic measures were associated with worsening of all cardiometabolic measures. Women had worsening LDL-cholesterol compared with men. Women with pre-intervention HbA1c > 6.8% and systolic blood pressure > 131 mm of Hg had worse post-intervention HbA1c and systolic blood pressure compared with men. Adding individual's neighborhood-level risks did not change effect sizes significantly. Conclusions: Increased cardiometabolic risks and gender were associated with worsening cardiometabolic outcomes. Understanding unresolved gender-specific needs and preferences of patients with increased cardiometabolic risks may aid in tailoring clinic-community-linked care planning.

Standardized Health data and Research Exchange (SHaRE): promoting a learning health system.

Aggregate de-identified data from electronic health records (EHRs) provide a valuable resource for research. The Standardized Health data and Research Exchange (SHaRE) is a diverse group of US healthcare organizations contributing to the Cerner Health Facts (HF) and Cerner Real-World Data (CRWD) initiatives. The 51 facilities at the 7 founding organizations have provided data about more than 4.8 million patients with 63 million encounters to HF and 7.4 million patients and 119 million encounters to CRWD. SHaRE organizations unmask their organization IDs and provide 3-digit zip code (zip3) data to support epidemiology and disparity research. SHaRE enables communication between members, facilitating data validation and collaboration as we demonstrate by comparing imputed EHR module usage to actual usage. Unlike other data sharing initiatives, no additional technology installation is required. SHaRE establishes a foundation for members to engage in discussions that bridge data science research and patient care, promoting the learning health system.

An index of geospatial disadvantage predicts both obesity and unmeasured body weight.

Neighborhood context impacts health. Using an index of geospatial disadvantage measures to predict neighborhood socioeconomic disparities would support area-based allocation of preventative resources, as well as the use of location as a clinical risk factor in care of individual patients. This study tested the association of the Area Deprivation Index (ADI), a neighborhood-based index of socioeconomic contextual disadvantage, with elderly obesity risk. We sampled 5066 Medicare beneficiaries at the University of Missouri between September 1, 2013 and September 1, 2014. We excluded patients with unknown street addresses, excluded body mass index (BMI) lower than 18 or higher than 62 as probable errors, and excluded patients with missing BMI data. We used a plot of simple proportions to examine the association between ADI and prevalence of obesity, defined as BMI of 30 and over. We found that obesity was significantly less prevalent in the least-disadvantaged ADI decile (decile 1) than in all other deciles (p < 0.05) except decile 7. Obesity prevalence within the other deciles (2-6 and 8-10) was not significantly distinguishable except that decile 2 was significantly lower than decile 4. Patients with missing BMI data were more likely to reside in the most disadvantaged areas. There was a positive association between neighborhood disadvantage and obesity in this Midwestern United States Medicare population. The association of missing BMI information with neighborhood disadvantage may reflect unmeasured gaps in care delivery to the most disadvantaged patients. These preliminary results support the continued study of neighborhood socioeconomic measures to identify health disparities in populations.

Longitudinal Changes in Risk Stratification for a Managed Population.

The LIGHT2 project managed the care of approximately 10,000 Medicare (primarily elderly) and Medicaid (low income) patients between 2013 and 2015. Risk tiers based on chronic disease diagnoses and recent healthcare utilization were strongly predictive of future healthcare utilization, and the authors expected that the members of an aging and well-insured population would gradually rise in risk of healthcare utilization over the course of three years. Various analytic techniques were used to characterize the members of higher risk tiers. However, retrospective cohort analysis and simple data visualization discovered the tendency of patients in lower initial risk tiers to remain healthy, and the tendency of patients in higher initial risk tiers to improve. In a time frame of three years, this return to stability was a more important influence on healthcare utilization than risk or aging.

The Paradox of Higher Charges for Lower-Risk Inpatient Admissions: When Healthier Patients Cost More.

Risk stratification is essential to achieving the Triple Aim of better health, better care, and lower costs. Although risk tiers based on chronic disease diagnoses and recent healthcare utilization were predictive of healthcare utilization and charges in a managed population, their correlation with specific high-cost outcomes was unknown. More detailed analyses were performed to confirm that admissions for higher-risk patients were more expensive. However, these analyses found that charges for admissions of high-risk patients were actually not more expensive but 33% less expensive. The billing categories of implants, surgery, and supplies accounted for 93% of this difference. These findings may reflect that high-risk patients are less often appropriate candidates for elective surgery. An understanding of this difference, especially if validated by claims data and replicated in other populations, may lead to important insights into using risk stratification for predicting health services utilization in managed care populations.

Combining Contrast Mining with Logistic Regression To Predict Healthcare Utilization in a Managed Care Population.

BACKGROUND: Because 5% of patients incur 50% of healthcare expenses, population health managers need to be able to focus preventive and longitudinal care on those patients who are at highest risk of increased utilization. Predictive analytics can be used to identify these patients and to better manage their care. Data mining permits the development of models that surpass the size restrictions of traditional statistical methods and take advantage of the rich data available in the electronic health record (EHR), without limiting predictions to specific chronic conditions. OBJECTIVE: The objective was to demonstrate the usefulness of unrestricted EHR data for predictive analytics in managed healthcare. METHODS: In a population of 9,568 Medicare and Medicaid beneficiaries, patients in the highest 5% of charges were compared to equal numbers of patients with the lowest charges. Contrast mining was used to discover the combinations of clinical attributes frequently associated with high utilization and infrequently associated with low utilization. The attributes found in these combinations were then tested by multiple logistic regression, and the discrimination of the model was evaluated by the c-statistic. RESULTS: Of 19,014 potential EHR patient attributes, 67 were found in combinations frequently associated with high utilization, but not with low utilization (support>20%). Eleven of these attributes were significantly associated with high utilization (p<0.05). A prediction model composed of these eleven attributes had a discrimination of 84%. CONCLUSIONS: EHR mining reduced an unusably high number of patient attributes to a manageable set of potential healthcare utilization predictors, without conjecturing on which attributes would be useful. Treating these results as hypotheses to be tested by conventional methods yielded a highly accurate predictive model. This novel, two-step methodology can assist population health managers to focus preventive and longitudinal care on those patients who are at highest risk for increased utilization.

Contrast Mining for Pattern Discovery and Descriptive Analytics to Tailor Sub-Groups of Patients Using Big Data Solutions.

The shift to electronic health records has created a plethora of information ready to be examined and acted upon by those in the medical and computational fields. While this allows for novel research on a scale unthinkable in the past, all discoveries still rely on some initial insight leading to a hypothesis. As the size and variety of data grows so do the number of potential findings, making it necessary to optimize hypothesis generation to increase the rate and importance of discoveries produced from the data. By using distributed Association Rule Mining and Contrast Mining in a big data ecosystem, it is possible to discover discrepancies within large, complex populations which are inaccessible using traditional methods. These discrepancies, when used as hypotheses, can help improve patient care through decision support, population health analytics, and other areas of healthcare.

The Effect of Neighborhood Disadvantage on Diabetes Prevalence.

Patient socioeconomic data is not usually included in medical records nor easily accessible to clinicians, yet socioeconomic disadvantage can be an important guide to disease management. This study evaluated the neighborhood-level Area Deprivation Index (ADI), a measure of neighborhood socioeconomic disadvantage, as a factor in diabetes mellitus prevalence. Electronic health records at an academic hospital system identified 4,770 Medicare beneficiaries. Logistic regression of diabetes diagnosis (ICD9=250.x) against ADI quintile, age, gender, and race/ethnicity found all these patient characteristics to be significantly associated. Diabetes prevalence was lowest in the least disadvantaged quintile of neighborhoods after adjusting for age, gender, and race/ethnicity. The positive non-linear association of diabetes prevalence with ADI demonstrates the power of this index to practically quantify socioeconomic disadvantage. The ADI may be suitable for clinical decision support, and for informing the policy changes which are needed to reduce socioeconomic disparities in diabetes prevalence and other health outcomes.

Identifying Patients at Risk of High Healthcare Utilization.

Objective. To develop a systematic and reproducible way to identify patients at increased risk for higher healthcare costs. Methods. Medical records were analyzed for 9,581 adults who were primary care patients in the University of Missouri Health System and who were enrolled in Medicare or Medicaid. Patients were categorized into one of four risk tiers as of October 1, 2013, and the four tiers were compared on demographic characteristics, number of healthcare episodes, and healthcare charges in the year before and the year after cohort formation. Results. The mean number of healthcare episodes and the sum of healthcare charges in the year following cohort formation were higher for patients in the higher-risk tiers. Conclusions. Retrospective information that is easily extracted from medical records can be used to create risk tiers that provide highly useful information about the prospective risk of healthcare utilization and costs.

Monitoring Resource Utilization in a Health Care Coordination Program.

PURPOSE OF THE STUDY: This initial article describes the development of a health care coordination intervention and documentation system designed using the Agency for Healthcare Research and Quality (AHRQ) Care Coordination Atlas framework for Centers for Medicare & Medicaid-funded innovation project, Leveraging Information Technology to Guide High-Tech, High-Touch Care (LIGHT). PRIMARY PRACTICE SETTING(S): The study occurred at an academic medical center that serves 114 counties. Twenty-five registered nurse care managers (NCMs) were hired to work with 137 providers in 10 family community and internal medicine clinics. METHODOLOGY AND SAMPLE: Patients were allocated into one of the four tiers on the basis of their chronic medical conditions and health care utilization. Using a documentation system on the basis of the AHRQ domains developed for this study, time and touch data were calculated for 8,593 Medicare, Medicaid, or dual-eligible patients. RESULTS: We discovered through the touch and time analysis that the majority of health care coordination activity occurred in the AHRQ domains of communication, assess needs and goals, and facilitate transitions, accounting for 79% of the NCM time and 61% of the touches. As expected, increasing tier levels resulted in increased use of NCM resources. Tier 3 accounted for roughly 16% of the patients and received 159 minutes/member (33% of total minutes), and Tier 4 accounted for 4% of patients and received 316 minutes/member (17% of all minutes). In contrast Tier 2, which did not require routine touches per protocol, had 5,507 patients (64%), and those patients received 5,246 hours of health care coordination, or 57 minutes/member, and took 48% of NCM time. IMPLICATIONS FOR CASE MANAGEMENT: 1. The AHRQ Care Coordination Atlas offered a systematic way to build a documentation system that allowed for the extraction of data that was used to calculate the amount of time and the number of touches that NCMs delivered per member. 2. Using a framework to systematically guide the work of health care coordination helped NCMs to think strategically about the care being delivered, and has implications for improving coordination of care. 3. For the purpose of reimbursement and communication with payers about quality metrics, it is vital that the type of touches and amount of time spent in delivering care coordination be documented in a manner that can be easily retrieved to guide practice decisions.

Self-efficacy and pain behavior among subjects with fibromyalgia.

Given the lack of objective physical measures for assessing fibromyalgia syndrome (FS), the role of pain assessment is particularly important. The role of psychological factors is controversial among FS patients. This study was designed to better understand the relationship between pain behaviors and psychological variables. Specifically, this study (1) refined a pain behavior observation (PBO) methodology for use with FS patients, (2) determined whether stretching is a valid pain behavior, and (3) assessed whether psychological variables including self-efficacy and/or depression can predict pain behaviors after controlling for disease severity and age. The 73 FS subjects meeting the American College of Rheumatology classification system completed questionnaires measuring self-efficacy, depression, and pain. Trained physicians conducted tender-point examinations. Subjects were video-taped using a standardized procedure. Two trained raters independently coded all pain behaviors. Kappa coefficients and correlations among pain behaviors and self-reported pain indicated that the PBO method was both reliable and valid. However, the newly defined pain behavior 'stretching' was found to be negatively associated with self-reported pain. Hierarchical multiple regression (MR) analyses revealed that depression did not predict pain behavior over and above myalgic scores and age; however, in 3 separate MR analyses, self-efficacy for function, pain, and other symptoms each predicted pain behavior over and above myalgic scores and age. This study indicated that the original pain behavior scoring methodology is appropriate for use with the FS population and should not be modified to include the pain behavior 'stretching'. Self-efficacy was related to pain behavior while depression was not among this FS sample.

Access to musculoskeletal specialists and resources in free and charitable clinics: a survey of clinic directors.

OBJECTIVE: To evaluate the capabilities and resources of free and charitable clinics in the United States to deliver musculoskeletal care to an indigent population. DESIGN: A voluntary, anonymous, cross-sectional survey. SETTING: Electronic mailing list for the National Association of Free and Charitable Clinics in September 2011, and in person at the Annual Summit for the National Association of Free and Charitable Clinics in October 2011. At the time of survey, 427 member-clinics were eligible for participation. PARTICIPANTS: One hundred forty-five (34%) respondents were included in data analysis. INTERVENTIONS: None. MAIN OUTCOME MEASURES: Answers to a questionnaire regarding access to musculoskeletal care. RESULTS: The average annual clinic volume was 5690 patient visits. Low back pain was the most common orthopedic complaint. Access to musculoskeletal specialty consultants was rated as poor or worse in 83% of clinics surveyed. The majority of respondents (63%) believed that their staff was able to treat only half or fewer of the patients who presented with musculoskeletal complaints in their clinic. The resource most needed to treat these conditions was musculoskeletal physician consultants. CONCLUSIONS: Indigent populations have a strong need for musculoskeletal care, but affordable access to physiatrists and other musculoskeletal specialists is extremely limited. Personnel at surveyed clinics believed that the greatest need to improve care is better access to these specialty physicians.

Relationship of psychiatric history to pain reports in rheumatoid arthritis.

OBJECTIVE: The purpose was to examine the relationship of pre-existing psychiatric history to pain reports in a cohort of persons with RA and concomitant major depression who were receiving a trial of antidepressant medication. METHOD: RA patients (n = 41) with a current episode of major depression were divided into two subgroups comprised of those with a previous psychiatric history (PSY+) (n = 20) and those without a previous psychiatric history (PSY-) (n = 21). The groups were compared with regard to their responsiveness to a regimen of antidepressive medication on measures of depression, pain, coping, and life stress over a period of 15 months. RESULTS: Although depression scores for both the PSY+ and the PSY- groups decreased significantly from baseline to 15-month follow-up, the composite pain score was found to be significantly decreased only for the PSY- group. CONCLUSION: Psychiatric history appears to predispose persons with concomitant RA and major depression to report less pain reduction following antidepressive treatment than those persons without a psychiatric history.

Analyzing reliability of change in depression among persons with rheumatoid arthritis.

OBJECTIVE: To examine several methods of determining reliability of change constructs in depressive symptoms in patients with rheumatoid arthritis (RA) and to demonstrate the strengths, weaknesses, and uses of each method. METHODS: Data were analyzed from a cohort of 54 persons with RA who participated in a combined behavioral/pharmacologic intervention of 15 months duration. These longitudinal data were used to examine 3 methodologies for assessing the reliability of change for various measures of depression. The specific methodologies involved the calculations of reliable change, sensitivity to change, and reliability of the change score. RESULTS: The analyses demonstrated differences in reliability of change performance across the various depression measures, which suggest that no single measure of depression for persons with RA should be considered superior in all contexts. CONCLUSION: The findings highlight the value of utilizing reliability of change constructs when examining changes in depressive symptoms over time.

Health status, cognitive coping, and depressive symptoms: testing for a mediator effect.

OBJECTIVE: Research has established a link between health status and symptoms of depression in persons with rheumatoid arthritis (RA), but the effects of "cognitive coping" variables have not been extensively studied. We examined the mediator effect of a cognitive coping variable (Pain Control and Rational Thinking factor score from the Coping Strategies Questionnaire) over the course of a pharmacological intervention. METHOD: Data were analyzed from 54 persons with RA, all of whom met diagnostic criteria for major depression. Measures of depression, health status, and cognitive coping were collected at 4 different stages of a pharmacological (antidepressant) study as follows: (1) at baseline, (2) postintervention, (3) 6 month followup, and (4) 15 month followup. RESULTS: Results indicated that a direct relationship existed between health status and depression at all 4 time periods. However, this relationship was mediated by cognitive coping only at the postintervention and the 6 month followup. CONCLUSION: A cognitive coping variable was found to mediate the relationship between health status and depression, but only at moderate levels of depression.

Research activities and perspectives of individuals completing a research enrichment program for physiatrists.

OBJECTIVE: To investigate the research activity and perspectives, and the predictors and barriers to research activity, in a cohort of individuals completing a research enrichment program for physiatrists. DESIGN: A retrospective cohort study design was utilized. Data collection was accomplished with a mailed survey, which was sent to 68 individuals who had completed the Research Enrichment Program for Physiatrists between 1991 and 1998. Data analysis was performed using both descriptive and inferential statistical methods. RESULTS: Eighty-five percent (58 of 68) of surveys were completed and returned. The majority of respondents were in academic-based practice (83%) at the assistant professor level (59%). Sixty-nine percent of the group reported spending no time in research, and 64% reported having no "protected" time for research. The mean number of peer-reviewed journal publications was 2.4, and the average number of research grants was 1.6, with 57% of the cohort reporting no grant funding. Departmental PhD, statistical, and secretarial support for research were all noted to be inadequate or not available for >50% of the cohort. High demand for clinical productivity, lack of protected research time, and lack of research funding were all identified as major barriers to research activity. Cluster analysis found greater research time and support to be associated with measures of research productivity. CONCLUSION: Long-term research success seems to require ongoing support, funding, and mentorship at the departmental and institutional level. Despite adequate training and motivation for research, research support was perceived as inadequate for many Research Enrichment Program graduates.

Assessment of depression in rheumatoid arthritis: a modified version of the center for epidemiologic studies depression scale.

OBJECTIVE: The Center for Epidemiologic Studies Depression Scale (CES-D) is an instrument commonly used to assess depressive symptoms. Although the psychometric properties of the instrument are well established, the instrument's ability to identify confirmed cases of major depression has been unclear. The purpose of this study was to evaluate the ability of cutoff scores from both a full scale and a modified CES-D to detect major depression in people with rheumatoid arthritis (RA). METHOD: Data were analyzed from 457 persons with RA, including 91 who met criteria for major depression. RESULTS: Results indicated that, in general, a full scale cutoff score of 19 was the most efficient in identifying cases of major depression; the cutoff score of 19 outperformed a variety of other cutoff scores from the modified scale. Even the most efficient cutoff scores, however, demonstrated problems in accurately identifying people with depression. CONCLUSION: The CES-D, while potentially useful as a screening tool, should not be used to identify cases of major depression.