Expenditures and Healthcare Utilization of Patients Receiving Care at a Specialized Primary Care Clinic Designed with and for Autistic Adults.

BACKGROUND: We previously found that autistic adults who received care through a primary care embedded specialized clinic, called the Center for Autism Services and Transition (CAST), had higher satisfaction, continuity of care, and preventive care use than national samples of autistic adults. OBJECTIVE: Examine the impact of CAST on healthcare utilization and expenditures. DESIGN: Retrospective study of medical billing data. SAMPLE: CAST patients (N = 490) were propensity score matched to Medicare-enrolled autistic adults (N = 980) and privately insured autistic adults (N = 980) using demographic characteristics. The median age of subjects was 21 years, 79% were male, and the median duration of observation was 2.2 years. MAIN MEASURES: We quantified expenditures and utilization for primary care; emergency department (ED) visits; inpatient hospitalizations; mental health admissions; and outpatient visits. KEY RESULTS: CAST patients had the highest primary care utilization and expenditures. However, CAST patients had significantly lower expenditures than Medicare-enrolled autistic adults for mental health admissions ($1074 vs $1903), outpatient visits ($1671 vs $2979), and total expenditures ($5893 vs $6987), as well as 57% fewer inpatient hospitalizations. Compared to privately insured autistic adults, CAST patients had significantly lower expenditures for mental health admissions ($1074 vs $1362), inpatient hospitalizations ($3851 vs $4513), and outpatient visits ($1671 vs $6070), as well as 16% fewer inpatient hospitalizations, 24% fewer ED visits, and 50% fewer outpatient visits. On average, CAST patients had more ED visits, mental health admissions, and outpatient visits than Medicare-enrolled autistic adults and more mental health admissions than privately insured autistic adults. CONCLUSIONS: Although CAST patients had greater primary care utilization and expenditures, our findings suggest embedding specialized clinics within broader primary care settings could be an alternative to current standards of care and may reduce expenditures and healthcare utilization in other areas, particularly relative to standard care for privately insured autistic adults.

Assessment of Fall-Related Emergency Medical Service Calls and Transports after a Community-Level Fall-Prevention Initiative.

Background: Getting effective fall prevention into the homes of medically and physically vulnerable individuals is a critical public health challenge. Community paramedicine is emerging globally as a new model of care that allows emergency medical service units to evaluate and treat patients in non-emergency contexts for prevention efforts and chronic care management. The promise of community paramedicine as a delivery system for fall prevention that scales to community-level improvements in outcomes is compelling but untested.Objective: To study the impact of a community paramedic program's optimization of a fall prevention system entailing a clinical pathway and learning health system (called Community-FIT) on community-level fall-related emergency medical service utilization rates.Methods: We used an implementation science framework and quality improvement methods to design and optimize a fall prevention model of care that can be embedded within community paramedic operations. The model was implemented and optimized in an emergency medical service agency servicing a Midwestern city in the United States (∼35,000 residents). Primary outcome measures included relative risk reduction in the number of community-level fall-related 9-1-1 calls and fall-related hospital transports. Interrupted time series analysis was used to evaluate relative risk reduction from a 12-month baseline period (September 2016 - August 2017) to a 12-month post-implementation period (September 2018-August 2019).Results: Community paramedic home visits increased from 25 in 2017, to 236 in 2018, to 517 in 2019, indicating a large increase in the number of households that benefited from the efforts. A relative risk reduction of 0.66 (95% [CI] 0.53, 0.76) in the number of fall calls and 0.63 (95% [CI] 0.46, 0.75) in the number of fall-related calls resulting in transports to the hospital were observed.Conclusions: Community-FIT may offer a powerful mechanism for community paramedics to reduce fall-related 9-1-1 calls and transports to hospitals that can be implemented in emergency medical agencies across the country.

Effects of a Specialized Primary Care Facility on Preventive Service Use Among Autistic Adults: a Retrospective Claims Study.

BACKGROUND: While in some studies, the patient-centered medical home has been linked with increased receipt of preventive services among other populations, there is a paucity of literature testing the effectiveness of medical homes in serving the healthcare needs of autistic adults. OBJECTIVE: To compare the receipt of preventive services by patients at a patient-centered medical home specifically designed for autistic adults (called the Center for Autism Services and Transition "CAST") to US national samples of autistic adults with private insurance or Medicare. DESIGN: Retrospective study of medical billing data. SAMPLE: The study sample included CAST patients (N = 490) who were propensity score matched to Medicare-enrolled autistic adults (N = 980) and privately insured autistic adults (N = 980) using demographic characteristics. The median age of subjects was 21 years old, 79% were male, and the median duration of observation was 2.2 years. MAIN MEASURES: The primary outcome measure was the receipt of any preventive service, as defined by the Medicare Learning Network and AAPC (formerly the American Academy of Professional Coders). Secondary outcome measures included receipt of specific preventive service types (i.e., general health and wellness services, screenings, counseling and therapies, vaccinations, and sexual/reproductive health services). KEY RESULTS: CAST patients had significantly greater odds of receiving any preventive service than Medicare-enrolled (OR = 10.3; 95% CI = 7.6-13.9) and privately insured (OR = 3.1; 95% CI = 2.3-4.2) autistic adults. CAST patients were also significantly more likely to receive screenings and vaccinations than either Medicare beneficiaries (screenings OR = 20.3; 95% CI = 14.7-28.0; vaccinations OR = 5.5; 95% CI = 4.3-7.0) or privately insured beneficiaries (screenings OR = 2.0; 95% CI = 1.6-2.5; vaccinations OR = 3.3; 95% CI = 2.6-4.1). CONCLUSIONS: Autistic adults receiving care through CAST were significantly more likely to recieve preventive care services than national samples of autistic adults. Future comparative effectiveness trials are needed to rigorously assess the impact of primary care-based initiatives to improve care for autistic adults.

Extraction of left ventricular ejection fraction information from various types of clinical reports.

Efforts to improve the treatment of congestive heart failure, a common and serious medical condition, include the use of quality measures to assess guideline-concordant care. The goal of this study is to identify left ventricular ejection fraction (LVEF) information from various types of clinical notes, and to then use this information for heart failure quality measurement. We analyzed the annotation differences between a new corpus of clinical notes from the Echocardiography, Radiology, and Text Integrated Utility package and other corpora annotated for natural language processing (NLP) research in the Department of Veterans Affairs. These reports contain varying degrees of structure. To examine whether existing LVEF extraction modules we developed in prior research improve the accuracy of LVEF information extraction from the new corpus, we created two sequence-tagging NLP modules trained with a new data set, with or without predictions from the existing LVEF extraction modules. We also conducted a set of experiments to examine the impact of training data size on information extraction accuracy. We found that less training data is needed when reports are highly structured, and that combining predictions from existing LVEF extraction modules improves information extraction when reports have less structured formats and a rich set of vocabulary.

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.

Chronic multisymptom illness among female Veterans deployed to Iraq and Afghanistan.

BACKGROUND: Chronic multisymptom illness (CMI) may be more prevalent among female Operation Enduring Freedom/Operation Iraqi Freedom/Operation New Dawn (OEF/OIF/OND) deployed Veterans due to deployment-related experiences. OBJECTIVES: To investigate CMI-related diagnoses among female OEF/OIF/OND Veterans. RESEARCH DESIGN: We estimated the prevalence of the International Classification of Disease-9th edition-Clinical Modification coded CMI-related diagnoses of chronic fatigue syndrome, fibromyalgia (FM), and irritable bowel syndrome (IBS) among female OEF/OIF/OND Veterans with Veterans Health Administration (VHA) visits, FY2002-2012 (n=78,435). We described the characteristics of female Veterans with and without CMI-related diagnoses and VHA settings of first CMI-related diagnoses. RESULTS: The prevalence of CMI-related diagnoses among female OEF/OIF/OND Veterans was 6397 (8.2%), over twice as high as the prevalence 95,424 (3.9%) among the totality of female Veterans currently accessing VHA (P<0.01). There were statistically significant differences in age, education, marital status, military component, service branch, and proportions of those with depression and/or post-traumatic stress disorder diagnoses across females with and without CMI-related diagnoses. Diagnoses were mainly from primary care, women's health, and physical medicine and rehabilitation clinics. CONCLUSIONS: CMI-related diagnoses were more prevalent among female OEF/OIF/OND Veterans compared with all female Veterans who currently access VHA. Future studies of the role of mental health diagnoses as confounders or mediators of the association of OEF/OIF/OND deployment and CMI are warranted. These and other factors associated with CMI may provide a basis for enhanced screening to facilitate recognition of these conditions. Further work should evaluate models of care and healthcare utilization related to CMI in female Veterans.

Publishing protocols for partnered research.

Published scientific protocols are advocated as a means of controlling bias in research reporting. Indeed, many journals require a study protocol with manuscript submission. However, publishing protocols of partnered research (PPR) can be challenging in light of the research model's dynamic nature, especially as no current reporting standards exist. Nevertheless, as these protocols become more prevalent, a priori documentation of methods in partnered research studies becomes increasingly important. Using as illustration a suite of studies aimed at improving coordination and communication in the primary care setting, we sought to identify challenges in publishing PPR relative to traditional designs, present alternative solutions to PPR publication, and propose an initial checklist of content to be included in protocols of partnered research. Challenges to publishing PPR include reporting details of research components intended to be co-created with operational partners, changes to sampling and entry strategy, and alignment of scientific and operational goals. Proposed solutions include emulating reporting standards of qualitative research, participatory action research, and adaptive trial designs, as well as embracing technological tools that facilitate publishing adaptive protocols, with version histories that are able to be updated as major protocol changes occur. Finally, we present a proposed checklist of reporting elements for partnered research protocols.

"Giving the patients less work": A thematic analysis of telehealth use and recommendations to improve usability for autistic adults.

LAY ABSTRACT: Real-time telehealth visits, called "virtual visits," are live video chats between patients and healthcare professionals. There are lots of steps involved in setting up a virtual visit, which may be difficult for some autistic adults. We interviewed 7 autistic adults, 12 family members of autistic adults, and 6 clinic staff from one clinic in the United States. Our goal was to understand their experiences with virtual visits and see how we can make virtual visits easier to use. We re-read text from the interviews to organize experiences and advice that was shared into topics. We found that autistic adults (or their family members) had to connect with clinic staff many times by phone or online over several days to set up a virtual visit. Participants said that having more experience with technology and using the online patient portal made virtual visits easier to use. But, having issues with technology before the visit could make autistic adults and family members anxious. Clinic staff said it was hard for them to meet the needs of people who were using virtual visits and those who were being seen in person at the clinic. Participants recommended reducing the number of calls between staff and autistic adults or family members using the online patient portal instead. Participants also recommended reminder messages, instruction videos, and approximate wait-times to help autistic adults and family members know what to expect for the virtual visit. Our results are based on peoples' experiences at one clinic, but could help other clinics make virtual visits easier to use for autistic adults and their family members.

Patient Dietary Supplements Use: Do Results from Natural Language Processing of Clinical Notes Agree with Survey Data?

There is widespread use of dietary supplements, some prescribed but many taken without a physician's guidance. There are many potential interactions between supplements and both over-the-counter and prescription medications in ways that are unknown to patients. Structured medical records do not adequately document supplement use; however, unstructured clinical notes often contain extra information on supplements. We studied a group of 377 patients from three healthcare facilities and developed a natural language processing (NLP) tool to detect supplement use. Using surveys of these patients, we investigated the correlation between self-reported supplement use and NLP extractions from the clinical notes. Our model achieved an F1 score of 0.914 for detecting all supplements. Individual supplement detection had a variable correlation with survey responses, ranging from an F1 of 0.83 for calcium to an F1 of 0.39 for folic acid. Our study demonstrated good NLP performance while also finding that self-reported supplement use is not always consistent with the documented use in clinical records.

"It was surprisingly equivalent to the appointment I had in person": Advantages and disadvantages of synchronous telehealth for delivering primary care for autistic adults.

LAY ABSTRACT: Autistic adults face many barriers to receiving quality primary health care like clinics that are far away and sensory sensitivities. Real-time telehealth visits, called "virtual visits," are live video chats between the patient and provider. Virtual visits may minimize barriers to care for autistic adults. We wanted to describe advantages and disadvantages of using virtual visits for delivering primary health care for autistic adults. We interviewed 7 autistic adults and 12 caregivers of autistic adults who receive primary care through one clinic. Autistic adults and caregivers said advantages to virtual visits were that (1) patients were more comfortable at home, (2) patients could get health care while avoiding physical contact with other people during the pandemic, and (3) virtual visits were similar to or better than in-person visits. The disadvantages included that (1) there could be technology problems like grainy video, (2) the doctor could not physically examine the patient (e.g. look in ears), and (3) patients sometimes participated less in the virtual visit than they would in person. Virtual visits may be beneficial for autistic adults by eliminating travel to the clinic and avoiding stressful sensory stimuli. We recognize that virtual visits may not work for all patients or in all situations. However, our study shows that primary care virtual visits may be beneficial for autistic adults during and beyond the pandemic.

Specialized primary care medical home: A positive impact on continuity of care among autistic adults.

LAY ABSTRACT: There is a nationally recognized need for innovative healthcare delivery models to improve care continuity for autistic adults as they age out of pediatric and into adult healthcare systems. One possible model of care delivery is called the "medical home". The medical home is not a residential home, but a system where a patient's healthcare is coordinated through a primary care physician to ensure necessary care is received when and where the patient needs it. We compared the continuity of care among autistic adult patients at a specialized primary care medical home designed to remove barriers to care for autistic adults, called the CAST, to matched national samples of autistic adults with private insurance or Medicare. Continuity of primary care among CAST patients was significantly better than that of matched national samples of autistic adult Medicare beneficiaries and similar to that of privately insured autistic adults. Our findings suggest that medical homes, like CAST, are a promising solution to improve healthcare delivery for the growing population of autistic adults.

Patient and caregiver experiences at a specialized primary care center for autistic adults.

Background: Little is known about the extent to which patient-centered medical homes meet the needs of autistic adults. Materials & methods: We conducted a cross-sectional survey of autistic adult patients (n = 47) and caregivers of autistic adult patients (n = 66) receiving care through one patient-centered medical home specifically designed to meet the needs of this population. We performed post hoc comparisons of our results to previously published data from a national sample of autistic adults. Results: Participants reported high levels of satisfaction with care, frequent preventive healthcare use and few unmet healthcare needs. Autistic adults in our sample reported significantly higher satisfaction and fewer unmet healthcare needs. Conclusion: A patient-centered medical home tailored to the needs of autistic adults is a promising approach to healthcare delivery for meeting this population's needs.

Development of a cardiac-centered frailty ontology.

BACKGROUND: A Cardiac-centered Frailty Ontology can be an important foundation for using NLP to assess patient frailty. Frailty is an important consideration when making patient treatment decisions, particularly in older adults, those with a cardiac diagnosis, or when major surgery is a consideration. Clinicians often report patient's frailty in progress notes and other documentation. Frailty is recorded in many different ways in patient records and many different validated frailty-measuring instruments are available, with little consistency across instruments. We specifically explored concepts relevant to decisions regarding cardiac interventions. We based our work on text found in a large corpus of clinical notes from the Department of Veterans Affairs (VA) national Electronic Health Record (EHR) database. RESULTS: The full ontology has 156 concepts, with 246 terms. It includes 86 concepts we expect to find in clinical documents, with 12 qualifier values. The remaining 58 concepts represent hierarchical groups (e.g., physical function findings). Our top-level class is clinical finding, which has children clinical history finding, instrument finding, and physical examination finding, reflecting the OGMS definition of clinical finding. Instrument finding is any score found for the existing frailty instruments. Within our ontology, we used SNOMED-CT concepts where possible. Some of the 86 concepts we expect to find in clinical documents are associated with the properties like ability interpretation. The concept ability to walk can either be able, assisted or unable. Each concept-property level pairing gets a different frailty score. Each scored concept received three scores: a frailty score, a relevance to cardiac decisions score, and a likelihood of resolving after the recommended intervention score. The ontology includes the relationship between scores from ten frailty instruments and frailty as assessed using ontology concepts. It also included rules for mapping ontology elements to instrument items for three common frailty assessment instruments. Ontology elements are used in two clinical NLP systems. CONCLUSIONS: We developed and validated a Cardiac-centered Frailty Ontology, which is a machine-interoperable description of frailty that reflects all the areas that clinicians consider when deciding which cardiac intervention will best serve the patient as well as frailty indications generally relevant to medical decisions. The ontology owl file is available on Bioportal at http://bioportal.bioontology.org/ontologies/CCFO .

Automated Extraction and Classification of Cancer Stage Mentions fromUnstructured Text Fields in a Central Cancer Registry.

Cancer stage is one of the most important prognostic parameters in most cancer subtypes. The American Joint Com-mittee on Cancer (AJCC) specifies criteria for staging each cancer type based on tumor characteristics (T), lymph node involvement (N), and tumor metastasis (M) known as TNM staging system. Information related to cancer stage is typically recorded in clinical narrative text notes and other informal means of communication in the Electronic Health Record (EHR). As a result, human chart-abstractors (known as certified tumor registrars) have to search through volu-minous amounts of text to extract accurate stage information and resolve discordance between different data sources. This study proposes novel applications of natural language processing and machine learning to automatically extract and classify TNM stage mentions from records at the Utah Cancer Registry. Our results indicate that TNM stages can be extracted and classified automatically with high accuracy (extraction sensitivity: 95.5%-98.4% and classification sensitivity: 83.5%-87%).

Incorporating Guideline Adherence and Practice Implementation Issues into the Design of Decision Support for Beta-Blocker Titration for Heart Failure.

BACKGROUND: The recognition of and response to undertreatment of heart failure (HF) patients can be complicated. A clinical reminder can facilitate use of guideline-concordant ß-blocker titration for HF patients with depressed ejection fraction. However, the design must consider the cognitive demands on the providers and the context of the work. OBJECTIVE: This study's purpose is to develop requirements for a clinical decision support tool (a clinical reminder) by analyzing the cognitive demands of the task along with the factors in the Cabana framework of physician adherence to guidelines, the health information technology (HIT) sociotechnical framework, and the Promoting Action on Research Implementation in Health Services (PARIHS) framework of health services implementation. It utilizes a tool that extracts information from medical records (including ejection fraction in free text reports) to identify qualifying patients at risk of undertreatment. METHODS: We conducted interviews with 17 primary care providers, 5 PharmDs, and 5 Registered Nurses across three Veterans Health Administration outpatient clinics. The interviews were based on cognitive task analysis (CTA) methods and enhanced through the inclusion of the Cabana, HIT sociotechnical, and PARIHS frameworks. The analysis of the interview data led to the development of requirements and a prototype design for a clinical reminder. We conducted a small pilot usability assessment of the clinical reminder using realistic clinical scenarios. RESULTS: We identified organizational challenges (such as time pressures and underuse of pharmacists), knowledge issues regarding the guideline, and information needs regarding patient history and treatment status. We based the design of the clinical reminder on how to best address these challenges. The usability assessment indicated the tool could help the decision and titration processes. CONCLUSION: Through the use of CTA methods enhanced with adherence, sociotechnical, and implementation frameworks, we designed a decision support tool that considers important challenges in the decision and execution of ß-blocker titration for qualifying HF patients at risk of undertreatment.

Application of contextual design methods to inform targeted clinical decision support interventions in sub-specialty care environments.

BACKGROUND & OBJECTIVES: In healthcare, the routine use of evidence-based specialty care management plans is mixed. Targeted computerized clinical decision support (CCDS) interventions can improve physician adherence, but adoption depends on CCDS' 'fit' within clinical work. We analyzed clinical work in outpatient and inpatient settings as a basis for developing guidelines for optimizing CCDS design. METHODS: The contextual design approach guided data collection, collation and analysis. Forty (40) consenting physicians were observed and interviewed in general internal medicine inpatient units and gastroenterology (GI) outpatient clinics at two academic medical centers. Data were collated using interpretive debriefing, and consolidated using thematic analysis and three work modeling approaches (communication flow, sequence and artifact models). RESULTS: Twenty-six consenting physicians were observed at Site A and 14 at Site B. Observations included attending (33%) and resident physicians. During research team debriefings, 220 of 341 unique topics were categorized into 5 CCDS-relevant themes. Resident physicians relied on patient assessment & planning processes to support their roles as communication and coordination hubs within the medical team. Artifact analysis further elucidated the evolution of assessment and planning over work shifts. CONCLUSIONS: The usefulness of CCDS tools may be enhanced in clinical care if the design: 1) accounts for clinical work that is distributed across people, space, and time; 2) targets communication and coordination hubs (specific roles) that can amplify the usefulness of CCDS interventions; 3) integrates CCDS with early clinical assessment & planning processes; and 4) provides CCDS in both electronic & hardcopy formats. These requirements provide a research agenda for future research in clinician-CCDS integration.

Predicting Adverse Outcomes in Heart Failure Patients Using Different Frailty Status Measures.

Frailty is an important outcome predictor in older patients. We randomly sampled 12,000 veterans with heart failure diagnosed in 2010. The topic modeling method was applied to identify frailty-related topics from the clinical notes in the electronic medical records. The frailty topics were classified into five deficit areas including physical functioning (PF), role-physical (RP), general health (GH), social functioning (SF), and mental health (MH). We experimented with different covariates and four different frailty measures: individual frailty topics, number of distinct frailty topics, a dichotomous deficit category, and the number of distinct deficits, respectively. A total of 8,531 (71.1%) patients had at least one frailty topic. The prevalence of GH, PF, MH, SF, and RP deficits were 89.0%, 61.3%, 56.9%, 40.6%, and 9.5%, respectively. PF deficits (yes/no) and the number of distinct deficits were the most consistent, significant predictors of adverse outcomes of rehosptalization or death.

Congestive heart failure information extraction framework for automated treatment performance measures assessment.

OBJECTIVE: This paper describes a new congestive heart failure (CHF) treatment performance measure information extraction system - CHIEF - developed as part of the Automated Data Acquisition for Heart Failure project, a Veterans Health Administration project aiming at improving the detection of patients not receiving recommended care for CHF. DESIGN: CHIEF is based on the Apache Unstructured Information Management Architecture framework, and uses a combination of rules, dictionaries, and machine learning methods to extract left ventricular function mentions and values, CHF medications, and documented reasons for a patient not receiving these medications. MEASUREMENTS: The training and evaluation of CHIEF were based on subsets of a reference standard of various clinical notes from 1083 Veterans Health Administration patients. Domain experts manually annotated these notes to create our reference standard. Metrics used included recall, precision, and the F 1 -measure. RESULTS: In general, CHIEF extracted CHF medications with high recall (>0.990) and good precision (0.960-0.978). Mentions of Left Ventricular Ejection Fraction were also extracted with high recall (0.978-0.986) and precision (0.986-0.994), and quantitative values of Left Ventricular Ejection Fraction were found with 0.910-0.945 recall and with high precision (0.939-0.976). Reasons for not prescribing CHF medications were more difficult to extract, only reaching fair accuracy with about 0.310-0.400 recall and 0.250-0.320 precision. CONCLUSION: This study demonstrated that applying natural language processing to unlock the rich and detailed clinical information found in clinical narrative text notes makes fast and scalable quality improvement approaches possible, eventually improving management and outpatient treatment of patients suffering from CHF.

Fibromyalgia syndrome care of Iraq- and Afghanistan-deployed Veterans in Veterans Health Administration.

Little is known regarding fibromyalgia syndrome (FMS) care among Operation Iraqi Freedom/Operation Enduring Freedom/Operation New Dawn (OIF/OEF/OND) Veterans. Current recommendations include interdisciplinary, team-based combined care approaches and limited opioid use. In this study of OIF/OEF/OND Veterans who accessed Veterans Health Administration services between 2002 and 2012, we hypothesized that combined care (defined as at least 4 primary care visits/yr with visits to mental health and/or rheumatology) versus <4 primary care visits/yr only would be associated with lower risk of at least 2 opioid prescriptions 12 mo following an FMS diagnosis. Using generalized linear models with a log-link, the Poisson family, and robust standard errors, we estimated risk ratios (RRs) and 95% confidence intervals (CIs). We found that 1% of Veterans had at least 2 FMS diagnoses (International Classification of Diseases-9th Revision-Clinical Modification code 729.1) or at least 1 FMS diagnosis by rheumatology. Veterans with (vs without) FMS were more likely to be female, older, Hispanic, and never/currently married. Combined primary, mental health, and rheumatology care was associated with at least 2 opioid prescriptions (RR [95% CI] for males 2.2 [1.1-4.4] and females 2.8 [0.4-18.6]). Also, combined care was associated with at least 2 nonopioid pain-related prescriptions, a practice supported by evidence-based clinical practice guidelines. In tandem, these results provide mixed evidence of benefit of combined care for FMS. Future studies of healthcare encounter characteristics, care coordination, and benefits for Veterans with FMS are needed.