Integrated Behavioral Health Implementation Patterns in Primary Care Using the Cross-Model Framework: A Latent Class Analysis

2021 ◽  
Author(s):  
Gretchen J. R. Buchanan ◽  
Timothy Piehler ◽  
Jerica Berge ◽  
Audrey Hansen ◽  
Kari A. Stephens
Keyword(s):  
Primary Care ◽  
Latent Class Analysis ◽  
Behavioral Health ◽  
Latent Class ◽  
Class Analysis ◽  
Model Framework ◽  
Cross Model ◽  
Integrated Behavioral Health ◽  
The Cross

scholarly journals Defining and measuring core processes and structures in integrated behavioral health in primary care: a cross-model framework

2020 ◽  
Vol 10 (3) ◽  
pp. 527-538
Author(s):  
Kari A Stephens ◽  
Constance van Eeghen ◽  
Brenda Mollis ◽  
Margaret Au ◽  
Stephanie A Brennhofer ◽  
...  
Keyword(s):  
Primary Care ◽  
Behavioral Health ◽  
Real World ◽  
Evidence Base ◽  
Healthcare Organizations ◽  
Model Framework ◽  
Cross Model ◽  
Integrated Behavioral Health ◽  
Usability Test ◽  
The Individual

Abstract A movement towards integrated behavioral health (IBH) in primary care continues to grow, among an accumulating evidence base for its effectiveness for improving care. However, healthcare organizations struggle to navigate where to target their limited resources for improving integration. We evaluated a cross-model framework of IBH core processes and structures. We used a mixed-methods approach for evaluation of the framework, which included (a) an evaluation survey of national experts and stakeholders, (b) crosswalks with common IBH measures, and (c) a real-world usability test. Five core IBH principles, mapping to 25 processes, and nine clinic structures were defined. Survey responses from 29 IBH domain and policy experts and stakeholders resulted in uniformly high ratings of importance and variable levels of feasibility for measurement, particularly with respect to electronic health record (EHR) systems. A real-world usability test resulted in good uptake and use of the framework across a state-wide effort. An IBH Cross-Model Framework of core principles, processes, and structures generated good acceptability and showed good real-world utility in a state-wide effort to improve IBH across disparate levels of integration in diverse primary care settings. Findings identify feasible areas of measurement, particularly with EHR systems. Next steps include testing the relationship between the individual framework components and patient outcomes to help guide clinics towards prioritizing efforts focused on improving integration.

scholarly journals Sleep disturbance subtypes in Parkinson’s disease based on the cross-culturally validated Korean version of the Parkinson’s Disease Sleep Scale-2

2019 ◽  
Author(s):  
Hui-Jun Yang ◽  
Han-Joon Kim ◽  
Seong-Beom Koh ◽  
Joong-Seok Kim ◽  
Tae-Beom Ahn ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Latent Class Analysis ◽  
Sleep Disturbance ◽  
Latent Class ◽  
Intraclass Correlation ◽  
Class Analysis ◽  
Korean Version ◽  
The Cross

Abstract Background: Sleep-related problems in Parkinson’s disease (PD) have received greater attention in recent years due to their clinical influence on morbidity, disability, and the health-related quality of life (HRQoL) of patients. This study aimed to evaluate the clinimetric properties of the Korean version of the Parkinson’s Disease Sleep Scale-2 (K-PDSS-2), and to analyze whether distinct sleep disturbance subtypes could be empirically identified in patients with PD based on the cross-culturally validated K-PDSS-2. Methods: The internal consistency, test-retest reliability, scale precision, and convergent validity of the K-PDSS-2 were assessed in a nationwide, multicenter study of 122 patients with PD. Latent class analysis (LCA) was used to derive subgroups of patients who experienced similar patterns of sleep-related problems and nocturnal disabilities. Results: The mean total K-PDSS-2 scores were 11.67 ± 9.87 (mean ± standard deviation) at baseline, and 12.61 ± 11.17 upon follow up testing. The Cronbach’s α coefficients of the total K-PDSS-2 score at baseline and at follow up testing were 0.851 and 0.880 respectively. Intraclass correlation coefficient over the 2-week period ranged from 0.672 to 0.848. The total K-PDSS-2 score was strongly correlated to HRQoL measures and other corresponding nonmotor scales. LCA indicated three distinct sleep disturbance classes in the study patients, namely “less troubled sleepers”, “PD-related nocturnal difficulties”, and “disturbed sleepers”. Conclusions: The K-PDSS-2 showed good clinimetric attributes in accordance with prior studies that were using the original version of the PDSS-2, therefore confirming the cross-cultural usefulness of the scale. Further, this study documents the first application of an LCA approach for identifying sleep disturbance subtypes in patients with PD. Keywords: Parkinson’s disease; sleep; PDSS-2; validity; reliability; Korean version; latent class analysis.

The covariation of multiple risk factors in primary care: a latent class analysis

2008 ◽  
Vol 31 (6) ◽  
pp. 525-535 ◽  
Author(s):  
Jennifer S. Funderburk ◽  
Stephen A. Maisto ◽  
Dawn E. Sugarman ◽  
Mike Wade
Keyword(s):  
Risk Factors ◽  
Primary Care ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Class Analysis ◽  
Multiple Risk Factors ◽  
Multiple Risk

scholarly journals Assessment of patient experience profiles and satisfaction with expectations of treatment effects by using latent class analysis based on a national patient experience survey in Taiwan

BMJ Open ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. e023045 ◽  
Author(s):  
Shang-Jyh Chiou ◽  
Pei-Chen Lee ◽  
Yu-Hsuan Chang ◽  
Pei-Shan Huang ◽  
Li-Hui Lee ◽  
...  
Keyword(s):  
Primary Care ◽  
Latent Class Analysis ◽  
Patient Experience ◽  
Treatment Effects ◽  
Latent Class ◽  
Symptom Improvement ◽  
Class Analysis ◽  
Cross Sectional ◽  
Patient Relationships ◽  
Physician Patient

ObjectivesHealth system responsiveness is a complicated issue that guides researchers wishing to design an efficient methodology for enhancing understanding of perspectives regarding healthcare systems. This study examined the relationship between patient experience profiles and satisfaction with expectations of treatment effects.DesignThis was a cross-sectional study. We used eight items obtained from latent class analysis to develop patient experience profiles.SettingPrimary care users in Taiwan.ParticipantsThis study conducted an annual National Health Insurance survey in Taiwan and sampled from those who had experience with the medical service in primary care clinics in 2015.Primary outcome measureRespondents were asked to indicate the extent of their satisfaction with their expectation of treatment effects (or symptom improvement).ResultsThe proportions of participants in groups 1–4 were 34%, 24%, 29% and 12%, respectively. Patients in good health were more satisfied with their expectations of treatment effects (OR 1.639, p=0.007). Furthermore, group 4 (-eAll) were less satisfied with their expectations of treatment effects than those in the other three groups (ORs: group 1 (+eAll): 9.81, group 2 (-CwR): 4.14 and group 3 (-CnR): 4.20).ConclusionsThe results revealed that experiences of poor accessibility and physician–patient relationships affected the patients’ expectations. Therefore, greater accessibility and more positive physician–patient relationships could lead to higher patient satisfaction with their expectations of treatment effects. Furthermore, the findings could assist authorities in targeting specific patients, with the objective of improving their healthcare service experience. They could also serve as a mechanism for improving the quality of healthcare services and increase accountability in healthcare practices.

scholarly journals Identifying subgroups of high-need, high-cost, chronically ill patients in primary care: A latent class analysis

PLoS ONE ◽  
2020 ◽  
Vol 15 (1) ◽  
pp. e0228103 ◽  
Author(s):  
Rowan G. M. Smeets ◽  
Arianne M. J. Elissen ◽  
Mariëlle E. A. L. Kroese ◽  
Niels Hameleers ◽  
Dirk Ruwaard
Keyword(s):  
Primary Care ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Chronically Ill ◽  
Class Analysis ◽  
Chronically Ill Patients

scholarly journals A latent class analysis of PTSD symptoms among inner city primary care patients

2018 ◽  
Vol 98 ◽  
pp. 1-8
Author(s):  
AKM Fazlur Rahman ◽  
Amita Manatunga ◽  
Ying Guo ◽  
Limin Peng ◽  
Megan Warnock ◽  
...  
Keyword(s):  
Primary Care ◽  
Inner City ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Class Analysis ◽  
Ptsd Symptoms ◽  
Primary Care Patients

scholarly journals The Utility of Latent Class Analysis to Understand Heterogeneity in Youth’s Coping Strategies: A Methodological Introduction

2021 ◽  
Author(s):  
Karen Nylund-Gibson ◽  
Adam C Garber ◽  
Jay Singh ◽  
Melissa R. Witkow ◽  
Adrienne Nishina ◽  
...  
Keyword(s):  
Latent Class Analysis ◽  
Coping Strategies ◽  
Social Stress ◽  
Structural Equation ◽  
Latent Class ◽  
Negative Mood ◽  
Auxiliary Variable ◽  
Equation Model ◽  
Class Analysis ◽  
Model Framework

Latent class analysis (LCA) is a useful statistical approach for understanding heterogeneity in a population. This paper provides a pedagogical introduction to LCA modeling and provides an example of its use to understand youth’s daily coping strategies. The analytic procedures are outlined for choosing the number of classes and integration of the LCA variable within a structural equation model framework, specifically a latent class moderation model, and a detailed table provides a summary of relevant modeling steps. This applied example demonstrates the modeling context when the LCA variable is moderating the association between a covariate and two outcome variables. Results indicate that students’ coping strategies moderate the association between social stress and negative mood, however they do not moderate the social stress-positive mood association. Appendices include R (MplusAutomation) code to automate the enumeration procedure, 3-step auxiliary variable integration, and the generation of figures for visually depicting LCA results.

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