Medical-surgical readmissions in patients with co-occurring serious mental illness: A systematic review and meta-analysis

Transitional care programs to improve the post-discharge experience of patients with multiple chronic conditions and co-occurring serious mental illness: A scoping review

The transition from hospital to home can be especially challenging for those with multiple chronic conditions and co-occurring serious mental illness (SMI). This population tends to be Medicaid-insured and disproportionately experiences health-related social needs. The aim of this scoping review was to identify the elements and outcomes of hospital-to-home transitional care programs for people diagnosed with SMI. A scoping review was conducted using Arksey and O'Malley's methodology. Three databases were searched; ten articles describing eight transitional care programs published from 2013 to 2024 met eligibility criteria. Five programs focused on patients being discharged from a psychiatric admission. Five of the interventions were delivered in the home. Intervention components included coaching services, medication management, psychiatric providers, and counseling. Program lengths ranged from one month to 90 days post-hospitalization. These programs evaluated quality of life, psychiatric symptoms, medication adherence, readmissions, and emergency department utilization. Notably, few programs appeared to directly address the unmet social needs of participants. While the focus and components of each transitional care program varied, there were overall positive improvements for participants in terms of improved quality of life, increased share decision making, and connections to primary and specialty care providers.

Improvements in Transitional Care Among Medicaid-Insured Patients With Serious Mental Illness

BACKGROUND

The Thrive program is an evidenced-based care model for Medicaid-insured adults in the hospital-to-home transition. A substantial portion of Thrive participants live with serious mental illness (SMI), yet Thrive's efficacy has not been tested among these patients.

PURPOSE

To compare 30-day postdischarge outcomes between Thrive participants with and without SMI and explore Thrive's appropriateness and acceptability among participants with SMI.

METHODS

We conducted a sequential explanatory mixed-methods study of 252 (62 with SMI) Thrive participants discharged from an academic medical center from February 2021 to August 2023. Interviews of participants with SMI were analyzed using rapid qualitative analysis.

RESULTS

Participants with and without SMI experienced similar rates of 30-day readmissions, emergency room visits, and postdischarge follow-up visits, with these differences being nonsignificant. Participants with SMI were highly satisfied with Thrive's care coordination and attention to social needs, yet participants suggested stronger connections to behavioral health care.

CONCLUSIONS

Participants with and without SMI benefit equitably from Thrive.

Medicare Changes in Response to COVID-19: Unintended Effects for Beneficiaries With Mental Illness or Substance Use Disorders

OBJECTIVE

The authors explored potential unintended consequences of Medicare policy changes in response to the COVID-19 pandemic for beneficiaries with behavioral health care needs.

METHODS

The authors collected policies relevant to mental health and substance use care. Informed by a literature review conducted in spring 2022, the authors convened a modified Delphi panel with 13 experts in June 2022. The authors assessed expert consensus through panelist surveys conducted before and after the panel convened.

RESULTS

Two policies that had a risk for unintended consequences for those with behavioral health care needs were identified. Panelists identified a discharge planning waiver as likely to decrease care access, care quality, and desirable outcomes and HIPAA enforcement discretion as likely to increase access to care and desirable outcomes (with some mixed effects on other outcomes) for Medicare beneficiaries with mental illness or substance use disorders.

CONCLUSIONS

Policies implemented quickly during the pandemic did not always account for unintended consequences for beneficiaries with behavioral health care needs.

The incidence rate of planned and emergency physical health hospital admissions in people diagnosed with severe mental illness: a cohort study

BACKGROUND

People with severe mental illness (SMI) have more physical health conditions than the general population, resulting in higher rates of hospitalisations and mortality. In this study, we aimed to determine the rate of emergency and planned physical health hospitalisations in those with SMI, compared to matched comparators, and to investigate how these rates differ by SMI diagnosis.

METHODS

We used Clinical Practice Research DataLink Gold and Aurum databases to identify 20,668 patients in England diagnosed with SMI between January 2000 and March 2016, with linked hospital records in Hospital Episode Statistics. Patients were matched with up to four patients without SMI. Primary outcomes were emergency and planned physical health admissions. Avoidable (ambulatory care sensitive) admissions and emergency admissions for accidents, injuries and substance misuse were secondary outcomes. We performed negative binomial regression, adjusted for clinical and demographic variables, stratified by SMI diagnosis.

RESULTS

Emergency physical health (aIRR:2.33; 95% CI 2.22-2.46) and avoidable (aIRR:2.88; 95% CI 2.60-3.19) admissions were higher in patients with SMI than comparators. Emergency admission rates did not differ by SMI diagnosis. Planned physical health admissions were lower in schizophrenia (aIRR:0.80; 95% CI 0.72-0.90) and higher in bipolar disorder (aIRR:1.33; 95% CI 1.24-1.43). Accident, injury and substance misuse emergency admissions were particularly high in the year after SMI diagnosis (aIRR: 6.18; 95% CI 5.46-6.98).

CONCLUSION

We found twice the incidence of emergency physical health admissions in patients with SMI compared to those without SMI. Avoidable admissions were particularly elevated, suggesting interventions in community settings could reduce hospitalisations. Importantly, we found underutilisation of planned inpatient care in patients with schizophrenia. Interventions are required to ensure appropriate healthcare use, and optimal diagnosis and treatment of physical health conditions in people with SMI, to reduce the mortality gap due to physical illness.

The impact of comorbid severe mental illness and common chronic physical health conditions on hospitalisation: A systematic review and meta-analysis

BACKGROUND

People with severe mental illness (SMI) are at higher risk of physical health conditions compared to the general population, however, the impact of specific underlying health conditions on the use of secondary care by people with SMI is unknown. We investigated hospital use in people managed in the community with SMI and five common physical long-term conditions: cardiovascular diseases, COPD, cancers, diabetes and liver disease.

METHODS

We performed a systematic review and meta-analysis (Prospero: CRD42020176251) using terms for SMI, physical health conditions and hospitalisation. We included observational studies in adults under the age of 75 with a diagnosis of SMI who were managed in the community and had one of the physical conditions of interest. The primary outcomes were hospital use for all causes, physical health causes and related to the physical condition under study. We performed random-effects meta-analyses, stratified by physical condition.

RESULTS

We identified 5,129 studies, of which 50 were included: focusing on diabetes (n = 21), cardiovascular disease (n = 19), COPD (n = 4), cancer (n = 3), liver disease (n = 1), and multiple physical health conditions (n = 2). The pooled odds ratio (pOR) of any hospital use in patients with diabetes and SMI was 1.28 (95%CI:1.15-1.44) compared to patients with diabetes alone and pooled hazard ratio was 1.19 (95%CI:1.08-1.31). The risk of 30-day readmissions was raised in patients with SMI and diabetes (pOR: 1.18, 95%CI:1.08-1.29), SMI and cardiovascular disease (pOR: 1.27, 95%CI:1.06-1.53) and SMI and COPD (pOR:1.18, 95%CI: 1.14-1.22) compared to patients with those conditions but no SMI.

CONCLUSION

People with SMI and five physical conditions are at higher risk of hospitalisation compared to people with that physical condition alone. Further research is warranted into the combined effects of SMI and physical conditions on longer-term hospital use to better target interventions aimed at reducing inappropriate hospital use and improving disease management and outcomes.

Innovations in Care Delivery for Patients With Serious Mental Illness Among Accountable Care Organizations

OBJECTIVE

This study examined whether and how organizations participating in accountable care organization (ACO) contracts integrate primary care and treatment for patients with serious mental illness.

METHODS

This study used responses to the 2017–2018 National Survey of ACOs (55% response rate) to measure ACO-reported use of three integrated care strategies: care manager to address physical health treatment coordination or nonmedical needs (e.g., job support and housing), patient registries to track physical health conditions, and primary care clinician colocated in a specialty mental health setting. Logistic regression was used to determine associations between ACO characteristics and strategy use.

RESULTS

Of 399 respondents who answered questions on integration, 303 (76%) reported using at least one integrated care strategy in at least one location. Use of care managers (defined by the respondent) was most common (N=281, 70%), followed by use of a patient registry (N=146, 37%) and colocation of a primary care clinician in a specialty mental health setting (N=118, 30%). Respondents reporting that their largest Medicaid contract or largest commercial contract included quality measures specific to serious mental illness (e.g., antipsychotic adherence) were more likely to use each integrated care delivery strategy. Self-reported use of three collaborative care strategies (care management, patient registry, or mental health consulting clinician) for treatment of depression or anxiety was associated with use of integrated primary care and treatment for serious mental illness.

CONCLUSIONS

In a national survey of ACOs, few respondents reported using either patient registries or primary care colocation to integrate primary care and treatment for serious mental illness.

Impact of Mental Health on General Surgery Patients and Strategies to Improve Outcomes

Mental Health Disorders (MHD) are a growing concern nationwide. The significant impact MHD have on surgical outcomes has only recently started to be understood. This literature review investigated how mental health impacts the outcomes of general surgery patients and what can be done to make improvements. Patients with schizophrenia had the poorest surgical outcomes. Mental health disorders increased post-surgical pain, hospital length of stay, complications, readmissions, and mortality. Mental health disorders decreased wound healing and quality of care. Optimizing outcomes will be best accomplished through integrating more effective perioperative screening tools and interventions. Screenings tools can incorporate artificial intelligence, MHD data, resilience and its biomarkers, and patient mental health questionnaires. Interventions include cognitive behavioral therapy, virtual reality, spirituality, pharmacology, and resilience training.

Individualized prediction of psychiatric readmissions for patients with major depressive disorder: a 10-year retrospective cohort study

Patients with major depressive disorder (MDD) are at high risk of psychiatric readmission while the factors associated with such adverse illness trajectories and the impact of the same factor at different follow-up times remain unclear. Based on machine learning (ML) approaches and real-world electronic medical records (EMR), we aimed to predict individual psychiatric readmission within 30, 60, 90, 180, and 365 days of an initial major depression hospitalization. In addition, we examined to what extent our prediction model could be made interpretable by quantifying and visualizing the features that drive the predictions at different follow-up times. By identifying 13,177 individuals discharged from a hospital located in western China between 2009 and 2018 with a recorded diagnosis of MDD, we established five prediction-modeling cohorts with different follow-up times. Four different ML models were trained with features extracted from the EMR, and explainable methods (SHAP and Break Down) were utilized to analyze the contribution of each of the features at both population-level and individual-level. The model showed a performance on the holdout testing dataset that decreased over follow-up time after discharge: AUC 0.814 (0.758-0.87) within 30 days, AUC 0.780 (0.728-0.833) within 60 days, AUC 0.798 (0.75-0.846) within 90 days, AUC 0.740 (0.687-0.794) within 180 days, and AUC 0.711 (0.676-0.747) within 365 days. Results add evidence that markers of depression severity and symptoms (recurrence of the symptoms, combination of key symptoms, the number of core symptoms and physical symptoms), along with age, gender, type of payment, length of stay, comorbidity, treatment patterns such as the use of anxiolytics, antipsychotics, antidepressants (especially Fluoxetine, Clonazepam, Olanzapine, and Alprazolam), physiotherapy, and psychotherapy, and vital signs like pulse and SBP, may improve prediction of psychiatric readmission. Some features can drive the prediction towards readmission at one follow-up time and towards non-readmission at another. Using such a model for decision support gives the clinician dynamic information of the patient's risk of psychiatric readmission and the specific features pulling towards readmission. This finding points to the potential of establishing personalized interventions that change with follow-up time.

Reducing costs and improving care after hospitalization: Economic evaluation of a novel transitional care clinic

Transitional care management (TCM) is a novel strategy for reducing costs and improving clinical outcomes after hospitalization but remains under-utilized. An economic analysis was performed on a hospital-based transition of care clinic (TCC) open to all patients regardless of payor status. TCC reduced re-hospitalization and emergency department (ED) utilization at six-month follow up. A cost-consequence analysis based on real world data found the TCC intervention to be cost effective relative to usual care. Hospital managers should consider adoption of TCC to improve patient care and reduce costs.

Behavioral health home impact on transitional care and readmissions among adults with serious mental illness

OBJECTIVE

To evaluate the impact of Maryland's behavioral health homes (BHHs) on receipt of follow-up care and readmissions following hospitalization among Medicaid enrollees with serious mental illness (SMI).

DATA SOURCES

Maryland Medicaid administrative claims for 12 232 individuals.

STUDY DESIGN

Weighted marginal structural models were estimated to account for time-varying exposure to BHH enrollment and time-varying confounders. These models compared changes over time in outcomes among BHH and comparison participants. Outcome measures included readmissions and follow-up care within 7 and 30 days following hospitalization.

DATA COLLECTION/EXTRACTION METHODS

Eligibility criteria included continuous enrollment in Medicaid for the first two years of the study period; 21-64 years; and use of psychiatric rehabilitation services.

PRINCIPAL FINDINGS

Over three years, BHH enrollment was associated with 3.8 percentage point (95% CI: 1.5, 6.1) increased probability of having a mental health follow-up service within 7 days of discharge from a mental illness-related hospitalization and 1.9 percentage point (95% CI: 0.0, 3.9) increased probability of having a general medical follow-up within 7 days of discharge from a somatic hospitalization. BHHs had no effect on probability of readmission.

CONCLUSIONS

BHHs may improve follow-up care for Medicaid enrollees with SMI, but effects do not translate into reduced risk of readmission.

Intention to Use Behavioral Health Data From a Health Information Exchange: Mixed Methods Study

BACKGROUND

Patients with co-occurring behavioral health and chronic medical conditions frequently overuse inpatient hospital services. This pattern of overuse contributes to inefficient health care spending. These patients require coordinated care to achieve optimal health outcomes. However, the poor exchange of health-related information between various clinicians renders the delivery of coordinated care challenging. Health information exchanges (HIEs) facilitate health-related information sharing and have been shown to be effective in chronic disease management; however, their effectiveness in the delivery of integrated care is less clear. It is prudent to consider new approaches to sharing both general medical and behavioral health information.

OBJECTIVE

This study aims to identify and describe factors influencing the intention to use behavioral health information that is shared through HIEs.

METHODS

We used a mixed methods design consisting of two sequential phases. A validated survey instrument was emailed to clinical and nonclinical staff in Alabama and Oklahoma. The survey captured information about the impact of predictors on the intention to use behavioral health data in clinical decision making. Follow-up interviews were conducted with a subsample of participants to elaborate on the survey results. Partial least squares structural equation modeling was used to analyze survey data. Thematic analysis was used to identify themes from the interviews.

RESULTS

A total of 62 participants completed the survey. In total, 63% (n=39) of the participants were clinicians. Performance expectancy (β=.382; P=.01) and trust (β=.539; P<.001) predicted intention to use behavioral health information shared via HIEs. The interviewees (n=5) expressed that behavioral health information could be useful in clinical decision making. However, privacy and confidentiality concerns discourage sharing this information, which is generally missing from patient records altogether. The interviewees also stated that training for HIE use was not mandatory; the training that was provided did not focus specifically on the exchange of behavioral health information.

CONCLUSIONS

Despite barriers, individuals are willing to use behavioral health information from HIEs if they believe that it will enhance job performance and if the information being transmitted is trustworthy. The findings contribute to our understanding of the role HIEs can play in delivering integrated care, particularly to vulnerable patients.

Serious mental illness, other mental health disorders, and outpatient health care as predictors of 30-day readmissions following medical hospitalization

OBJECTIVE

Prior research has not addressed whether both serious mental illness (SMI) and other mental health (OMH) disorders affect the likelihood of 30-day readmissions after medical hospitalizations, or whether post-discharge use of outpatient medical, mental health, and pharmacy services is associated with readmission likelihood.

METHODS

Using the Truven Health Analytics MarketScan® Medicaid Multi-State Database, we studied 43,817 Medicaid beneficiaries, age 18-64, following discharge from medical hospitalizations in 2011. Logistic regression models compared all-cause, 30-day readmissions among those with SMI, OMH, and no psychiatric diagnosis, and examined associations of 30-day outpatient service use with 30-day readmissions.

RESULTS

Thirty-day readmission rates were 15.9% (SMI), 13.8% (OMH), and 11.7% (no mental illness). In multivariable analysis, compared to patients without mental illness, odds of readmission were greater for those with SMI (aOR = 1.43, 95%CI:1.32-1.51) and OMH (aOR = 1.21, 95%CI:1.12-1.30), and lower among those using outpatient mental health services (aOR = 0.50, 95%CI: 0.44-0.56).

CONCLUSION

The adult Medicaid population disproportionately includes patients with SMI and OMH disorders, both of which were found to be associated with 30-day hospital readmissions. Receiving outpatient mental health services after hospital discharge may be protective against readmission following medical hospitalizations, suggesting the need for further research on these topics.

Impact of Co-occurring Mental Disorders and Chronic Physical Illnesses on Frequency of Emergency Department Use and Hospitalization for Mental Health Reasons

Background: Patients with mental disorders (MD) are at high risk for a wide range of chronic physical illnesses (CPI), often resulting in greater use of acute care services. This study estimated risk of emergency department (ED) use and hospitalization for mental health (MH) reasons among 678 patients with MD and CPI compared to 1,999 patients with MD only. Methods: Patients visiting one of six Quebec (Canada) ED for MH reasons and at onset of a MD in 2014-15 (index year) were included. Negative binomial models comparing the two groups estimated risk of ED use and hospitalization at 12-month follow-up to index ED visit, controlling for clinical, sociodemographic, and service use variables. Results: Patients with MD, more severe overall clinical conditions and those who received more intensive specialized MH care had higher risks of frequent ED use and hospitalization. Continuity of medical care protected against both ED use and hospitalization, while general practitioner (GP) consultations protected against hospitalization only. Patients aged 65+ had lower risk of ED use, whereas risk of hospitalization was higher for the 45-64- vs. 12-24-year age groups, and for men vs. women. Conclusion: Strategies including assertive community treatment, intensive case management, integrated co-occurring treatment, home treatment, and shared care may improve adequacy of care for patients with MD-CPI, as well as those with MD only whose clinical profiles were severe. Prevention and outreach strategies may also be promoted, especially among men and older age groups.

Factors associated with 30-day readmissions following medical hospitalizations among Medicaid beneficiaries with schizophrenia, bipolar disorder, and major depressive disorder

While evidence suggests that adults with serious mental illness have an elevated rate of 30-day readmissions after medical hospitalizations, most studies are of patients who are privately insured or Medicare beneficiaries, and little is known about the differential experiences of people with schizophrenia, bipolar disorder, and major depression. We used the Truven Health Analytics MarketScan® Medicaid Multi-State Database to study 43,817 Medicaid enrollees from 11 states, age 18-64, who were discharged from medical hospitalizations in 2011. Our outcome was unplanned all-cause readmissions within 30 days of discharge. In a multivariable analysis, compared to those with no SMI, people with schizophrenia had the highest odds of 30-day readmission (aOR: 1.46, 95% CI: 1.33-1.59), followed by those with bipolar disorder (aOR: 1.25, 95% CI: 1.14-1.38), and those with major depressive disorder (aOR: 1.18, 95% CI: 1.06-1.30). Readmissions also were more likely among those with substance use disorders, males, those with Medicaid eligibility due to disability, patients with longer index hospitalizations, and those with 2 or more medical co-morbidities. This is the first large-scale study to demonstrate the elevated risk of hospital readmission among low-income, working-age adults with schizophrenia. Given their greater psychological, social, and economic vulnerability, our findings can be used to design transition interventions and service delivery systems that address their complex needs.

Hospitalization outcomes of people who use drugs: One size does not fit all

People with opioid use disorder (OUD) have worse hospital outcomes and higher healthcare costs. There are rising reports of people with OUD also using other classes of drugs, however patterns of substance use have not been evaluated for differential effects on hospital outcomes. We performed a data-analysis of the Healthcare Utilization Project's National Readmissions Database, examining the effects of patterns of substance use, age, gender, and diagnosis on the outcomes of Against Medical Advice (AMA) discharges and 30-day readmissions. About one-third of the patients with OUD who were admitted to the hospital had at least one additional substance use disorder (SUD). Thirteen percent of persons with OUD were discharged AMA, and 12% were readmitted to the hospital within 30 days of discharge. Compared to people with OUD alone, people who used stimulants had increased odds of AMA discharge (aOR 1.83 (CI 1.73, 1.96)) and 30-day readmission (aOR 1.30 (95% CI 1.23, 1.37)). Multiple concomitant substance use disorders were associated with increased odds of AMA discharge and 30-day readmission. Conclusions: People with OUD have high rates of both AMA discharges and 30 day-readmissions, and there is a layered effect of increasing co-occurring SUDs leading to worse hospitalization outcomes. The heterogeneity of drug use patterns needs to be considered when developing strategies to improve health care outcomes for people with substance use disorder.