A needs-based method for estimating the behavioral health staff needs of community health centers

Burke, Bridget Teevan; Miller, Benjamin F.; Proser, Michelle; Petterson, Stephen; Bazemore, Andrew; Goplerud, Eric; Phillips, Robert L.

doi:10.1186/1472-6963-13-245

Cited by 52 publications

(36 citation statements)

References 28 publications

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“…Bruckner and colleagues [ 109 ] used epidemiological data on the prevalence of adult and paediatric disorders, and assumptions regarding treatment coverage and service delivery to estimate the shortfall in mental health service providers in 58 low and middle income countries. Burke and colleagues [ 114 ] used staffing data, current service use patterns and national survey data to estimate the number of patients likely to need behavioural health care in US public health centres. They estimated the number of visits likely needed by health centre patients annually, and the number of full-time equivalent providers needed to serve them.…”

Section: Introductionmentioning

confidence: 99%

Preventing mental illness: closing the evidence-practice gap through workforce and services planning

Furber

Segal

Leach

et al. 2015

BMC Health Serv Res

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BackgroundMental illness is prevalent across the globe and affects multiple aspects of life. Despite advances in treatment, there is little evidence that prevalence rates of mental illness are falling. While the prevention of cardiovascular disease and cancers are common in the policy dialogue and in service delivery, the prevention of mental illness remains a neglected area. There is accumulating evidence that mental illness is at least partially preventable, with increasing recognition that its antecedents are often found in infancy, childhood, adolescence and youth, creating multiple opportunities into young adulthood for prevention. Developing valid and reproducible methods for translating the evidence base in mental illness prevention into actionable policy recommendations is a crucial step in taking the prevention agenda forward.MethodBuilding on an aetiological model of adult mental illness that emphasizes the importance of intervening during infancy, childhood, adolescence and youth, we adapted a workforce and service planning framework, originally applied to diabetes care, to the analysis of the workforce and service structures required for best-practice prevention of mental illness.ResultsThe resulting framework consists of 6 steps that include identifying priority risk factors, profiling the population in terms of these risk factors to identify at-risk groups, matching these at-risk groups to best-practice interventions, translation of these interventions to competencies, translation of competencies to workforce and service estimates, and finally, exploring the policy implications of these workforce and services estimates. The framework outlines the specific tasks involved in translating the evidence-base in prevention, to clearly actionable workforce, service delivery and funding recommendations.ConclusionsThe framework describes the means to deliver mental illness prevention that the literature indicates is achievable, and is the basis of an ongoing project to model the workforce and service structures required for mental illness prevention.

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Section: Introductionmentioning

confidence: 99%

Preventing mental illness: closing the evidence-practice gap through workforce and services planning

Furber

Segal

Leach

et al. 2015

BMC Health Serv Res

View full text Add to dashboard Cite

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“…The first three are traditional approaches commonly used, [10,[16][17][18][19][20][21] while the latter is an approach proposed by the authors. A thorough comparison and evaluation are important because each approach introduces unique assumptions which have implications for the reliability of the forecast within the context of its application.…”

Section: Health Workforce Forecasting Approachesmentioning

confidence: 99%

Comparing health workforce forecasting approaches for healthcare planning: The case for ophthalmologists

Ansah

Koh

Korne

et al. 2017

IJH

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Health workforce planning is essential in the provision of quality healthcare. Several approaches to planning are customarily used and advocated, each with unique underlying assumptions. Thus, a thorough understanding of each assumption is required in order to make an informed decision on the choice of forecasting approach to be used. For illustration, we compare results for eye care requirements in Singapore using three established workforce forecasting approaches -workforce-to-population-ratio, needs based approach, utilization based approach -and a proposed robust integrated approach to discuss the appropriateness of each approach under various scenarios. Four simulation models using the systems modeling methodology of system dynamics were developed for use in each approach. These models were initialized and simulated using the example of eye care workforce planning in Singapore, to project the number of ophthalmologists required up to the year 2040 under the four different approaches. We found that each approach projects a different number of ophthalmologists required over time. The needs based approach tends to project the largest number of required ophthalmologists, followed by integrated, utilization based and workforce-to-population ratio approaches in descending order. The four different approaches vary widely in their forecasted workforce requirements and reinforce the need to be discerning of the fundamental differences of each approach in order to choose the most appropriate one. Further, health workforce planning should also be approached in a comprehensive and integrated manner that accounts for developments in demographic and healthcare systems.

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“…Primary care represents an important strategy for treating depression in populations facing health disparities with limited or no access to specialty mental health care. The prevalence of psychiatric disorders in Federally Qualified Health Centers (FQHCs) is substantially higher than in private primary care settings [8] because FQHCs, commonly referred to as the nation's medical "safety net", serve a disproportionate number of low-income patients [9]. However, the vast majority of FQHCs do not have psychiatrists or psychologists practicing on site [10].…”

mentioning

confidence: 99%

Implementing collaborative care to reduce depression for rural native American/Alaska native people

Bowen

Powers

Russo

et al. 2020

BMC Health Serv Res

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Background: The purpose of this study was to identify the effects of Collaborative Care on rural Native American and Alaska Native (AI/AN) patients. Methods: Collaborative Care was implemented in three AI/AN serving clinics. Clinic staff participated in training and coaching designed to facilitate practice change. We followed clinics for 2 years to observe improvements in depression treatment and to examine treatment outcomes for enrolled patients. Collaborative Care elements included universal screening for depression, evidence-based treatment to target, use of behavioral health care managers to deliver the intervention, use of psychiatric consultants to provide caseload consultation, and quality improvement tracking to improve and maintain outcomes. We used t-tests to evaluate the main effects of Collaborative Care and used multiple linear regression to better understand the predictors of success. We also collected qualitative data from members of the Collaborative Care clinical team about their experience. Results: The clinics participated in training and practice coaching to implement Collaborative Care for depressed patients. Depression response (50% or greater reduction in depression symptoms as measured by the PHQ-9) and remission (PHQ-9 score less than 5) rates were equivalent in AI/AN patients as compared with White patients in the same clinics. Significant predictors of positive treatment outcome include only one depression treatment episodes during the study and more follow-up visits per patient. Clinicians were overall positive about their experience and the effect on patient care in their clinic. Conclusions: This project showed that it is possible to deliver Collaborative Care to AI/AN patients via primary care settings in rural areas.

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A needs-based method for estimating the behavioral health staff needs of community health centers

Cited by 52 publications

References 28 publications

Preventing mental illness: closing the evidence-practice gap through workforce and services planning

Preventing mental illness: closing the evidence-practice gap through workforce and services planning

Comparing health workforce forecasting approaches for healthcare planning: The case for ophthalmologists

Implementing collaborative care to reduce depression for rural native American/Alaska native people

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