Criminal Justice and Behavioral Health Care Costs of Mental Health Court Participants: A Six-Year Study

2014 ◽  
Vol 65 (9) ◽  
pp. 1100-1104 ◽  
Author(s):  
Henry J. Steadman ◽  
Lisa Callahan ◽  
Pamela Clark Robbins ◽  
Roumen Vesselinov ◽  
Thomas G. McGuire ◽  
...  
Keyword(s):  
Mental Health ◽  
Health Care ◽  
Criminal Justice ◽  
Behavioral Health ◽  
Health Care Costs ◽  
Behavioral Health Care ◽  
Mental Health Court ◽  
Care Costs

The Digital Revolution in Behavioral Health

2019 ◽  
Vol 26 (1) ◽  
pp. 102-111 ◽  
Author(s):  
Michael J Hasselberg
Keyword(s):  
Mental Health ◽  
Health Care ◽  
Mental Health Care ◽  
Behavioral Health ◽  
Digital Health ◽  
Behavioral Health Care ◽  
The United States ◽  
Care Providers ◽  
Digital Revolution ◽  
The University

BACKGROUND: Technology is disrupting every modern industry, from supermarkets to car manufacturing, and is now entering the health care space. Technological innovations in psychiatry include the opportunity for conducting therapy via two-way video conferencing, providing electronic consultations, and telementoring and education of community health care providers. Use of mobile health applications is also an expanding area of interest and promise. OBJECTIVE: The purpose of this article is to review the evolution and pros and cons of technology-enabled health care since the digital movement in psychiatry began more than 50 years ago as well as describe the University of Rochester’s innovative digital behavioral health care model. METHODS: A review of the literature and recent reports on innovations in digital behavioral health care was conducted, along with a review of the University of Rochester’s model to describe the current state of digital behavioral health care. RESULTS: Given the lack of access to care and mental health professional shortages in many parts of the United States, particularly rural areas, digital behavioral health care will be an increasingly important strategy for managing mental health care needs. However, there are numerous hurdles to be overcome in adopting digital health care, including provider resistance and knowledge gaps, lack of reimbursement parity, restrictive credentialing and privileging, and overregulation at both the state and federal levels. CONCLUSIONS: Digital health innovations are transforming the delivery of mental health care services and psychiatric mental health nurses can be on the forefront of this important digital revolution.

Primary Care Practice Redesign: Challenges in Improving Behavioral Health Care for a Vulnerable Patient Population

2019 ◽  
Vol 35 (2) ◽  
pp. 101-109
Author(s):  
Deborah Swavely ◽  
David T. O’Gurek ◽  
Veronica Whyte ◽  
Alexandra Schieber ◽  
Daohai Yu ◽  
...  
Keyword(s):  
Mental Health ◽  
Health Care ◽  
Behavioral Health ◽  
Severe Depression ◽  
Behavioral Health Care ◽  
Urban Setting ◽  
Care Practice ◽  
Control Group ◽  
Post Traumatic Stress ◽  
Significant Difference

This study examined a program focused on integrating mental health in a family medicine practice in an economically challenged urban setting. The program included using a behavioral health technology platform, a behavioral health collaborative composed of community mental health agencies, and a community health worker (CHW). Of the 202 patients screened, 196 were used for analysis; 56% were positive for anxiety, 38% had scores consistent with moderate to severe depression, and 34% were positive for post-traumatic stress disorder. There was a statistically significant difference in the diagnosis of depression when comparing the screened group to a control group. Only 27% of patients followed through with behavioral health referrals despite navigational assistance provided by a CHW and assured access to care through a community agency engaged with the Behavioral Health Alliance. Further qualitative analysis revealed that there were complex patient factors that affected patient decision making regarding follow-up with behavioral health care.

scholarly journals Direct Health Care Costs of Treating Seasonal Affective Disorder: A Comparison of Light Therapy and Fluoxetine

2012 ◽  
Vol 2012 ◽  
pp. 1-5 ◽  
Author(s):  
Amy Cheung ◽  
Carolyn Dewa ◽  
Erin E. Michalak ◽  
Gina Browne ◽  
Anthony Levitt ◽  
...  
Keyword(s):  
Mental Health ◽  
Health Care ◽  
Mental Health Care ◽  
Affective Disorder ◽  
Health Care Costs ◽  
Seasonal Affective Disorder ◽  
Light Therapy ◽  
Major Depressive Episodes ◽  
Depressive Episodes ◽  
Care Costs

Objective.To compare the direct mental health care costs between individuals with Seasonal Affective Disorder randomized to either fluoxetine or light therapy.Methods.Data from the CANSAD study was used. CANSAD was an 8-week multicentre double-blind study that randomized participants to receive either light therapy plus placebo capsules or placebo light therapy plus fluoxetine. Participants were aged 18–65 who met criteria for major depressive episodes with a seasonal (winter) pattern. Mental health care service use was collected for each subject for 4 weeks prior to the start of treatment and for 4 weeks prior to the end of treatment. All direct mental health care services costs were analysed, including inpatient and outpatient services, investigations, and medications.Results.The difference in mental health costs was significantly higher after treatment for the light therapy group compared to the medication group—a difference of $111.25 (z=−3.77,P=0.000). However, when the amortized cost of the light box was taken into the account, the groups were switched with the fluoxetine group incurring greater direct care costs—a difference of $75.41 (z=−2.635,P=0.008).Conclusion.The results suggest that individuals treated with medication had significantly less mental health care cost after-treatment compared to those treated with light therapy.

Government Resources on Mental Health

2020 ◽  
Vol 35 (12) ◽  
pp. 573-573
Author(s):  
Paul Baldwin
Keyword(s):  
Mental Health ◽  
Health Care ◽  
Clinical Research ◽  
Health Care Costs ◽  
Health Care Services ◽  
Disease Prevalence ◽  
Statistical Information ◽  
Care Services ◽  
Care Costs

State and federal governments collect massive amounts of data, both in their role as sponsors of research and as payers for an increasing share of health care services. The information available includes definitive clinical research as well as statistical information about disease prevalence and contribution to health care costs.

scholarly journals How Much Do Mental Health and Substance Use/Addiction Affect Use of General Medical Services? Extent of Use, Reason for Use, and Associated Costs

2016 ◽  
Vol 62 (1) ◽  
pp. 48-56 ◽  
Author(s):  
Kathryn Graham ◽  
Joyce Cheng ◽  
Sharon Bernards ◽  
Samantha Wells ◽  
Jürgen Rehm ◽  
...  
Keyword(s):  
Mental Health ◽  
Primary Care ◽  
Health Care ◽  
Substance Use ◽  
Health Care Costs ◽  
Emergency Departments ◽  
Primary Care Physicians ◽  
Service Use ◽  
Cross Sectional ◽  
Care Costs

Objective: To measure service use and costs associated with health care for patients with mental health (MH) and substance use/addiction (SA) problems. Methods: A 5-year cross-sectional study (2007-2012) of administrative health care data was conducted (average annual sample size = 123,235 adults aged >18 years who had a valid Ontario health care number and used at least 1 service during the year; 55% female). We assessed average annual use of primary care, emergency departments and hospitals, and overall health care costs for patients identified as having MH only, SA only, co-occurring MH and SA problems (MH+SA), and no MH and/or SA (MH/SA) problems. Total visits/admissions and total non-MH/SA visits (i.e., excluding MH/SA visits) were regressed separately on MH, SA, and MH+SA cases compared to non-MH/SA cases using the 2011-2012 sample ( N = 123,331), controlling for age and sex. Results: Compared to non-MH/SA patients, MH/SA patients were significantly ( P < 0.001) more likely to visit primary care physicians (1.82 times as many visits for MH-only patients, 4.24 for SA, and 5.59 for MH+SA), use emergency departments (odds, 1.53 [MH], 3.79 [SA], 5.94 [MH+SA]), and be hospitalized (odds, 1.59 [MH], 4.10 [SA], 7.82 [MH+SA]). MH/SA patients were also significantly more likely than non-MH/SA patients to have non-MH/SA-related visits and accounted for 20% of the sample but over 30% of health care costs. Conclusions: MH and SA are core issues for all health care settings. MH/SA patients use more services overall and for non-MH/SA issues, with especially high use and costs for MH+SA patients.

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