Alignment of patient and primary care practice member perspectives of chronic illness care: a cross-sectional analysis

Polly H. Noel and Luci K. Leykum are with the South Texas Veterans Health Care System, 7400 Merton Minter Blvd, San Antonio, TX 78229, USA -- Polly H. Noel, Ray F. Palmer, Raquel L. Romero, Luci K. Leykum, Holly J. Lanham, and Krista W. Bowers are with the Department of Medicine, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Dr, San Antonio, TX 78229, USA -- Michael L. Parchman is with the MacColl Center for Healthcare Innovation, Group Health Research Institute, Group Health Cooperative, 1730 Minor Ave 1600, Seattle, WA 98101, USA -- Holly J. Leykum is with the The McCombs School of Business, The University of Texas at Austin, 2110 Speedway, Stop B6000, Austin, TX 78712, USA -- John E. Zeber is with the Central Texas Veterans Health Care System, 1901 S. 1st St, Temple, TX 76504, USA and Scott and White Healthcare Center for Applied Health Research, 2401 S. 31st St, Temple, TX 76508, USABackground: Little is known as to whether primary care teams’ perceptions of how well they have implemented the Chronic Care Model (CCM) corresponds with their patients’ own experience of chronic illness care. We examined the extent to which practice members’ perceptions of how well they organized to deliver care consistent with the CCM were associated with their patients’ perceptions of the chronic illness care they have received. Methods: Analysis of baseline measures from a cluster randomized controlled trial testing a practice facilitation intervention to implement the CCM in small, community-based primary care practices. All practice “members” (i.e., physician providers, non-physician providers, and staff) completed the Assessment of Chronic Illness Care (ACIC) survey and adult patients with 1 or more chronic illnesses completed the Patient Assessment of Chronic Illness Care (PACIC) questionnaire. Results: Two sets of hierarchical linear regression models accounting for nesting of practice members (N = 283) and patients (N = 1,769) within 39 practices assessed the association between practice member perspectives of CCM implementation (ACIC scores) and patients’ perspectives of CCM (PACIC). ACIC summary score was not significantly associated with PACIC summary score or most of PACIC subscale scores, but four of the ACIC subscales were consistently associated with PACIC summary score and the majority of PACIC subscale scores after controlling for patient characteristics. The magnitude of the coefficients, however, indicates that the level of association is weak. Conclusions: The ACIC and PACIC scales appear to provide complementary and relatively unique assessments of how well clinical services are aligned with the CCM. Our findings underscore the importance of assessing both patient and practice member perspectives when evaluating quality of chronic illness care.Information, Risk, and Operations Management (IROM)[email protected]

The Challenges of Multimorbidity from the Patient Perspective

BACKGROUND Although multiple co-occurring chronic illnesses within the same individual are increasingly common, few studies have examined the challenges of multimorbidity from the patient perspective. OBJECTIVE The aim of this study is to examine the self-management learning needs and willingness to see non-physician providers of patients with multimorbidity compared to patients with single chronic illnesses. DESIGN. This research is designed as a cross-sectional survey. PARTICIPANTS Based upon ICD-9 codes, patients from a single VHA healthcare system were stratified into multimorbidity clusters or groups with a single chronic illness from the corresponding cluster. Nonproportional sampling was used to randomly select 720 patients. MEASUREMENTS Demographic characteristics, functional status, number of contacts with healthcare providers, components of primary care, self-management learning needs, and willingness to see nonphysician providers. RESULTS Four hundred twenty-two patients returned surveys. A higher percentage of multimorbidity patients compared to single morbidity patients were "definitely" willing to learn all 22 self-management skills, of these only 2 were not significant. Compared to patients with single morbidity, a significantly higher percentage of patients with multimorbidity also reported that they were "definitely" willing to see 6 of 11 non-physician healthcare providers. CONCLUSIONS Self-management learning needs of multimorbidity patients are extensive, and their preferences are consistent with team-based primary care. Alternative methods of providing support and chronic illness care may be needed to meet the needs of these complex patients.US Department of Veterans Affairs (01-110, 02-197); Agency for Healthcare Research and Quality (K08 HS013008-02

Patterns of primary care and mortality among patients with schizophrenia or diabetes: a cluster analysis approach to the retrospective study of healthcare utilization

Abstract Background Patients with schizophrenia have difficulty managing their medical healthcare needs, possibly resulting in delayed treatment and poor outcomes. We analyzed whether patients reduced primary care use over time, differentially by diagnosis with schizophrenia, diabetes, or both schizophrenia and diabetes. We also assessed whether such patterns of primary care use were a significant predictor of mortality over a 4-year period. Methods The Veterans Healthcare Administration (VA) is the largest integrated healthcare system in the United States. Administrative extracts of the VA's all-electronic medical records were studied. Patients over age 50 and diagnosed with schizophrenia in 2002 were age-matched 1:4 to diabetes patients. All patients were followed through 2005. Cluster analysis explored trajectories of primary care use. Proportional hazards regression modelled the impact of these primary care utilization trajectories on survival, controlling for demographic and clinical covariates. Results Patients comprised three diagnostic groups: diabetes only (n = 188,332), schizophrenia only (n = 40,109), and schizophrenia with diabetes (Scz-DM, n = 13,025). Cluster analysis revealed four distinct trajectories of primary care use: consistent over time, increasing over time, high and decreasing, low and decreasing. Patients with schizophrenia only were likely to have low-decreasing use (73% schizophrenia-only vs 54% Scz-DM vs 52% diabetes). Increasing use was least common among schizophrenia patients (4% vs 8% Scz-DM vs 7% diabetes) and was associated with improved survival. Low-decreasing primary care, compared to consistent use, was associated with shorter survival controlling for demographics and case-mix. The observational study was limited by reliance on administrative data. Conclusion Regular primary care and high levels of primary care were associated with better survival for patients with chronic illness, whether psychiatric or medical. For schizophrenia patients, with or without comorbid diabetes, primary care offers a survival benefit, suggesting that innovations in treatment retention targeting at-risk groups can offer significant promise of improving outcomes.http://deepblue.lib.umich.edu/bitstream/2027.42/78274/1/1472-6963-9-127.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78274/2/1472-6963-9-127.pdfPeer Reviewe

How Does Facilitation in Healthcare Work? Using Mechanism Mapping to Illuminate the Black Box of a Meta-Implementation Strategy

BACKGROUND: Healthcare facilitation, an implementation strategy designed to improve the uptake of effective clinical innovations in routine practice, has produced promising yet mixed results in randomized implementation trials and has not been fully researched across different contexts. OBJECTIVE: Using mechanism mapping, which applies directed acyclic graphs that decompose an effect of interest into hypothesized causal steps and mechanisms, we propose a more concrete description of how healthcare facilitation works to inform its further study as a meta-implementation strategy. METHODS: Using a modified Delphi consensus process, co-authors developed the mechanistic map based on a three-step process. First, they developed an initial logic model by collectively reviewing the literature and identifying the most relevant studies of healthcare facilitation components and mechanisms to date. Second, they applied the logic model to write vignettes describing how facilitation worked (or did not) based on recent empirical trials that were selected via consensus for inclusion and diversity in contextual settings (US, international sites). Finally, the mechanistic map was created based on the collective findings from the vignettes. FINDINGS: Theory-based healthcare facilitation components informing the mechanistic map included staff engagement, role clarification, coalition-building through peer experiences and identifying champions, capacity-building through problem solving barriers, and organizational ownership of the implementation process. Across the vignettes, engagement of leaders and practitioners led to increased socialization of the facilitator\u27s role in the organization. This in turn led to clarifying of roles and responsibilities among practitioners and identifying peer experiences led to increased coherence and sense-making of the value of adopting effective innovations. Increased trust develops across leadership and practitioners through expanded capacity in adoption of the effective innovation by identifying opportunities that mitigated barriers to practice change. Finally, these mechanisms led to eventual normalization and ownership of the effective innovation and healthcare facilitation process. IMPACT: Mapping methodology provides a novel perspective of mechanisms of healthcare facilitation, notably how sensemaking, trust, and normalization contribute to quality improvement. This method may also enable more efficient and impactful hypothesis-testing and application of complex implementation strategies, with high relevance for lower-resourced settings, to inform effective innovation uptake

A pilot survey of post-deployment health care needs in small community-based primary care clinics

<p>Abstract</p> <p>Background</p> <p>Relatively little is known regarding to what extent community-based primary care physicians are encountering post-deployment health care needs among veterans of the Afghanistan or Iraq conflicts and their family members.</p> <p>Methods</p> <p>This pilot study conducted a cross-sectional survey of 37 primary care physicians working at small urban and suburban clinics belonging to a practice-based research network in the south central region of Texas.</p> <p>Results</p> <p>Approximately 80% of the responding physicians reported caring for patients who have been deployed to the Afghanistan or Iraq war zones, or had a family member deployed. Although these physicians noted a variety of conditions related to physical trauma, mental illnesses and psychosocial disruptions such as marital, family, financial, and legal problems appeared to be even more prevalent among their previously deployed patients and were also noted among family members of deployed veterans.</p> <p>Conclusions</p> <p>Community-based primary care physicians should be aware of common post-deployment health conditions and the resources that are available to meet these needs.</p

A group randomized trial of a complexity-based organizational intervention to improve risk factors for diabetes complications in primary care settings: study protocol

<p>Abstract</p> <p>Background</p> <p>Most patients with type 2 diabetes have suboptimal control of their glucose, blood pressure (BP), and lipids – three risk factors for diabetes complications. Although the chronic care model (CCM) provides a roadmap for improving these outcomes, developing theoretically sound implementation strategies that will work across diverse primary care settings has been challenging. One explanation for this difficulty may be that most strategies do not account for the complex adaptive system (CAS) characteristics of the primary care setting. A CAS is comprised of individuals who can learn, interconnect, self-organize, and interact with their environment in a way that demonstrates non-linear dynamic behavior. One implementation strategy that may be used to leverage these properties is practice facilitation (PF). PF creates time for learning and reflection by members of the team in each clinic, improves their communication, and promotes an individualized approach to implement a strategy to improve patient outcomes.</p> <p>Specific objectives</p> <p>The specific objectives of this protocol are to: evaluate the effectiveness and sustainability of PF to improve risk factor control in patients with type 2 diabetes across a variety of primary care settings; assess the implementation of the CCM in response to the intervention; examine the relationship between communication within the practice team and the implementation of the CCM; and determine the cost of the intervention both from the perspective of the organization conducting the PF intervention and from the perspective of the primary care practice.</p> <p>Intervention</p> <p>The study will be a group randomized trial conducted in 40 primary care clinics. Data will be collected on all clinics, with 60 patients in each clinic, using a multi-method assessment process at baseline, 12, and 24 months. The intervention, PF, will consist of a series of practice improvement team meetings led by trained facilitators over 12 months. Primary hypotheses will be tested with 12-month outcome data. Sustainability of the intervention will be tested using 24 month data. Insights gained will be included in a delayed intervention conducted in control practices and evaluated in a pre-post design.</p> <p>Primary and secondary outcomes</p> <p>To test hypotheses, the unit of randomization will be the clinic. The unit of analysis will be the repeated measure of each risk factor for each patient, nested within the clinic. The repeated measure of glycosylated hemoglobin A1c will be the primary outcome, with BP and Low Density Lipoprotein (LDL) cholesterol as secondary outcomes. To study change in risk factor level, a hierarchical or random effect model will be used to account for the nesting of repeated measurement of risk factor within patients and patients within clinics.</p> <p>This protocol follows the CONSORT guidelines and is registered per ICMJE guidelines:</p> <p>Clinical Trial Registration Number</p> <p>NCT00482768</p

Understanding the implementation of evidence-based care: A structural network approach

<p>Abstract</p> <p>Background</p> <p>Recent study of complex networks has yielded many new insights into phenomenon such as social networks, the internet, and sexually transmitted infections. The purpose of this analysis is to examine the properties of a network created by the 'co-care' of patients within one region of the Veterans Health Affairs.</p> <p>Methods</p> <p>Data were obtained for all outpatient visits from 1 October 2006 to 30 September 2008 within one large Veterans Integrated Service Network. Types of physician within each clinic were nodes connected by shared patients, with a weighted link representing the number of shared patients between each connected pair. Network metrics calculated included edge weights, node degree, node strength, node coreness, and node betweenness. Log-log plots were used to examine the distribution of these metrics. Sizes of k-core networks were also computed under multiple conditions of node removal.</p> <p>Results</p> <p>There were 4,310,465 encounters by 266,710 shared patients between 722 provider types (nodes) across 41 stations or clinics resulting in 34,390 edges. The number of other nodes to which primary care provider nodes have a connection (172.7) is 42% greater than that of general surgeons and two and one-half times as high as cardiology. The log-log plot of the edge weight distribution appears to be linear in nature, revealing a 'scale-free' characteristic of the network, while the distributions of node degree and node strength are less so. The analysis of the k-core network sizes under increasing removal of primary care nodes shows that about 10 most connected primary care nodes play a critical role in keeping the <it>k</it>-core networks connected, because their removal disintegrates the highest <it>k</it>-core network.</p> <p>Conclusions</p> <p>Delivery of healthcare in a large healthcare system such as that of the US Department of Veterans Affairs (VA) can be represented as a complex network. This network consists of highly connected provider nodes that serve as 'hubs' within the network, and demonstrates some 'scale-free' properties. By using currently available tools to explore its topology, we can explore how the underlying connectivity of such a system affects the behavior of providers, and perhaps leverage that understanding to improve quality and outcomes of care.</p

Primary Language, Income and the Intensification of Anti-glycemic Medications in Managed Care: the (TRIAD) Study

BACKGROUND Patients who speak Spanish and/or have low socioeconomic status are at greater risk of suboptimal glycemic control. Inadequate intensification of anti-glycemic medications may partially explain this disparity. OBJECTIVE To examine the associations between primary language, income, and medication intensification. DESIGN Cohort study with 18-month follow-up. PARTICIPANTS One thousand nine hundred and thirty-nine patients with Type 2 diabetes who were not using insulin enrolled in the Translating Research into Action for Diabetes Study (TRIAD), a study of diabetes care in managed care. MEASUREMENTS Using administrative pharmacy data, we compared the odds of medication intensification for patients with baseline A1c ≥ 8%, by primary language and annual income. Covariates included age, sex, race/ethnicity, education, Charlson score, diabetes duration, baseline A1c, type of diabetes treatment, and health plan. RESULTS Overall, 42.4% of patients were taking intensified regimens at the time of follow-up. We found no difference in the odds of intensification for English speakers versus Spanish speakers. However, compared to patients with incomes 75,000 (OR 2.22, 1.53-3.24) had increased odds of intensification. This latter pattern did not differ statistically by race. CONCLUSIONS Low-income patients were less likely to receive medication intensification compared to higher-income patients, but primary language (Spanish vs. English) was not associated with differences in intensification in a managed care setting. Future studies are needed to explain the reduced rate of intensification among low income patients in managed care

Sequencing, de novo annotation and analysis of the first Anguilla anguilla transcriptome: EeelBase opens new perspectives for the study of the critically endangered european eel

Background: Once highly abundant, the European eel (Anguilla anguilla L.; Anguillidae; Teleostei) is considered to be critically endangered and on the verge of extinction, as the stock has declined by 90-99% since the 1980s. Yet, the species is poorly characterized at molecular level with little sequence information available in public databases.\ud \ud Results: The first European eel transcriptome was obtained by 454 FLX Titanium sequencing of a normalized cDNA library, produced from a pool of 18 glass eels (juveniles) from the French Atlantic coast and two sites in the Mediterranean coast. Over 310,000 reads were assembled in a total of 19,631 transcribed contigs, with an average length of 531 nucleotides. Overall 36% of the contigs were annotated to known protein/nucleotide sequences and 35 putative miRNA identified.\ud \ud Conclusions: This study represents the first transcriptome analysis for a critically endangered species. EeelBase, a dedicated database of annotated transcriptome sequences of the European eel is freely available at http://compgen.bio.unipd.it/eeelbase. Considering the multiple factors potentially involved in the decline of the European eel, including anthropogenic factors such as pollution and human-introduced diseases, our results will provide a rich source of data to discover and identify new genes, characterize gene expression, as well as for identification of genetic markers scattered across the genome to be used in various applications