Impact of Behavioral Health Screening on Proactive Identification of Patients at Risk for Hospital Readmission

Integration of patient experience factors improves readmission prediction

Many readmission prediction models have marginal accuracy and are based on clinical and demographic data that exclude patient response data. The objective of this study was to evaluate the accuracy of a 30-day hospital readmission prediction model that incorporates patient response data capturing the patient experience. This was a prospective cohort study of 30-day hospital readmissions. A logistic regression model to predict readmission risk was created using patient responses obtained during interviewer-administered questionnaires as well as demographic and clinical data. Participants (N = 846) were admitted to 2 inpatient adult medicine units at Massachusetts General Hospital from 2012 to 2016. The primary outcome was the accuracy (measured by receiver operating characteristic) of a 30-day readmission risk prediction model. Secondary analyses included a readmission-focused factor analysis of individual versus collective patient experience questions. Of 1754 eligible participants, 846 (48%) were enrolled and 201 (23.8%) had a 30-day readmission. Demographic factors had an accuracy of 0.56 (confidence interval [CI], 0.50-0.62), clinical disease factors had an accuracy of 0.59 (CI, 0.54-0.65), and the patient experience factors had an accuracy of 0.60 (CI, 0.56-0.64). Taken together, their combined accuracy of receiver operating characteristic = 0.78 (CI, 0.74-0.82) was significantly more accurate than these factors were individually. The individual accuracy of patient experience, demographic, and clinical data was relatively poor and consistent with other risk prediction models. The combination of the 3 types of data significantly improved the ability to predict 30-day readmissions. This study suggests that more accurate 30-day readmission risk prediction models can be generated by including information about the patient experience.

Factors Associated With Unplanned Acute Care Services for Patients With Newly Diagnosed Hematologic Malignancies

PURPOSE

This study evaluated risk factors predicting unplanned 30-day acute service utilization among adults subsequent to hospitalization for a new diagnosis of leukemia, lymphoma, or myeloma. This study explored the prevalence of medical complications (aligned with OP-35 measure specifications from the Centers for Medicare & Medicaid Services [CMS] Hospital Outpatient Quality Reporting Program) and the potential impact of psychosocial factors on unplanned acute care utilization.

METHODS

This study included 933 unique patients admitted to three acute care inpatient facilities within a nonprofit community-based health care system in southern California from 2012 to 2017. Integrated comprehensive data elements from electronic medical records and facility oncology registries were leveraged for univariate statistics, predictive models constructed using multivariable logistic regression, and further exploratory data mining, with predictive accuracy of the models measured with c-statistics.

RESULTS

The mean age of study participants was 65 years, and 55.1% were male. Specific diagnoses were lymphoma (48.7%), leukemia (35.2%), myeloma (14.0%), and mixed types (2.1%). Approximately one fifth of patients received unplanned acute care services within 30 days postdischarge, and over half of these patients presented with one or more symptoms associated with the CMS medical complication measure. The predictive models, with c-statistics ranging from 0.7 and above for each type of hematologic malignancy, indicated good predictive qualities with the impact of psychosocial functioning on the use of acute care services (P values < .05), including lack of consult for social work during initial admission (lymphoma or myeloma), history of counseling or use of psychotropic medications (lymphoma), and past substance use (myeloma).

CONCLUSION

This study provides insights into patient-related factors that may inform a proactive approach to improve health outcomes, such as enhanced care transition, monitoring, and support interventions.

The prevalence, grouping, and distribution of stressors and their association with anxiety among hospitalized patients

Anxiety is prevalent among hospital inpatients and it has harmful effects on patient well-being and clinical outcomes. We aimed to characterize the sources of hospital distress and their relationship to anxiety. We conducted a cross-sectional study of inpatients (n = 271) throughout two Southeastern U.S. metropolitan hospitals. Participants completed a survey to identify which of 38 stressors they were experiencing. They also completed the State Trait Anxiety Inventory six-item scale. We evaluated the prevalence of stressors, their distribution, and crude association with anxiety. We then used multivariate logistic regression to estimate the association between stressors and clinically relevant anxiety, with and without adjusting for demographic variables. We used factor analysis to describe the interrelationships among stressors and to examine whether groups of stressors tend to be endorsed together. The following stressors were highly endorsed across all unit types: pain, being unable to sleep, feelings of frustration, being overwhelmed, and fear of the unknown. Stressors relating to isolation/meaninglessness and fear/frustration tend to be endorsed together. Stressors were more frequently endorsed by younger, female, and uninsured or Medicaid-insured patients and being female and uninsured was associated with anxiety in bivariate analysis. After controlling for the sources of distress in multivariate linear analysis, gender and insurance status no longer predicted anxiety. Feelings of isolation, lack of meaning, frustration, fear, or a loss of control were predictive. Study results suggest that multiple stressors are prevalent among hospital inpatients and relatively consistent across hospital unit and disease type. Interventions for anxiety or emotional/spiritual burden may be best targeted to stressors that are frequently endorsed or associated with anxiety, especially among young and female patients.

Implementation stages in practice: A review of behavioral health innovation within hospitals

Behavioral health influences patient health outcomes and healthcare utilization rates. Hospitals are promising settings for appropriate identification, treatment, and referral of behavioral health issues and may affect hospital admission rates and healthcare costs. Implementation frameworks are designed to aid successful adoption and scaling of health innovations. One type - process models - present staged frameworks for rolling out an innovation into routine practice. Process models are appealing for their pragmatism but are criticized for oversimplifying the complexity of implementation. This review investigates the empirical evidence for process models' utility in hospitals, chosen for their uniquely complex structures, by determining whether their use impacts implementation outcomes. Using systematic search and selection criteria across six databases, ten peer-reviewed studies were identified. Each applied a process model for implementing behavioral health innovations within hospital systems. Studies were coded by type of stage framework and reported implementation outcomes. Studies reported mostly favorable or mixed outcomes. No one framework prevailed in use nor evidence. Due to the paucity of published literature and reported data, there is limited evidence that process model application propels implementation outcomes in hospital settings. Furthering the science requires creating and utilizing systematic guidelines to employ process models, measure and report implementation stage transition, and measure and report implementation outcomes. Management and practitioners can include such data collection in standard process evaluations of hospital implementation and scale-up activities, or adopt complexity-informed approaches that lack the simplicity of process models but may be more realistic for complex settings.

Use of electronic medical records in development and validation of risk prediction models of hospital readmission: systematic review

OBJECTIVE

To provide focused evaluation of predictive modeling of electronic medical record (EMR) data to predict 30 day hospital readmission.

DESIGN

Systematic review.

DATA SOURCE

Ovid Medline, Ovid Embase, CINAHL, Web of Science, and Scopus from January 2015 to January 2019.

ELIGIBILITY CRITERIA FOR SELECTING STUDIES

All studies of predictive models for 28 day or 30 day hospital readmission that used EMR data.

OUTCOME MEASURES

Characteristics of included studies, methods of prediction, predictive features, and performance of predictive models.

RESULTS

Of 4442 citations reviewed, 41 studies met the inclusion criteria. Seventeen models predicted risk of readmission for all patients and 24 developed predictions for patient specific populations, with 13 of those being developed for patients with heart conditions. Except for two studies from the UK and Israel, all were from the US. The total sample size for each model ranged between 349 and 1 195 640. Twenty five models used a split sample validation technique. Seventeen of 41 studies reported C statistics of 0.75 or greater. Fifteen models used calibration techniques to further refine the model. Using EMR data enabled final predictive models to use a wide variety of clinical measures such as laboratory results and vital signs; however, use of socioeconomic features or functional status was rare. Using natural language processing, three models were able to extract relevant psychosocial features, which substantially improved their predictions. Twenty six studies used logistic or Cox regression models, and the rest used machine learning methods. No statistically significant difference (difference 0.03, 95% confidence interval -0.0 to 0.07) was found between average C statistics of models developed using regression methods (0.71, 0.68 to 0.73) and machine learning (0.74, 0.71 to 0.77).

CONCLUSIONS

On average, prediction models using EMR data have better predictive performance than those using administrative data. However, this improvement remains modest. Most of the studies examined lacked inclusion of socioeconomic features, failed to calibrate the models, neglected to conduct rigorous diagnostic testing, and did not discuss clinical impact.

Assess the Performance and Cost-Effectiveness of LACE and HOSPITAL Re-Admission Prediction Models as a Risk Management Tool for Home Care Patients: An Evaluation Study of a Medical Center Affiliated Home Care Unit in Taiwan

The LACE index and HOSPITAL score models are the two most commonly used prediction models identifying patients at high risk of readmission with limited information for home care patients. This study compares the effectiveness of these two models in predicting 30-day readmission following acute hospitalization of such patients in Taiwan. A cohort of 57 home care patients were enrolled and followed-up for one year. We compared calibration, discrimination (area under the receiver operating curve, AUC), and net reclassification improvement (NRI) to identify patients at risk of 30-day readmission for both models. Moreover, the cost-effectiveness of the models was evaluated using microsimulation analysis. A total of 22 readmissions occurred after 87 acute hospitalizations during the study period (readmission rate = 25.2%). While the LACE score had poor discrimination (AUC = 0.598, 95% confidence interval (CI) = 0.488–0.702), the HOSPITAL score achieved helpful discrimination (AUC = 0.691, 95% CI = 0.582–0.785). Moreover, the HOSPITAL score had improved the risk prediction in 38.3% of the patients, compared with the LACE index (NRI = 0.383, 95% CI = 0.068–0.697, p = 0.017). Both prediction models effectively reduced readmission rates compared to an attending physician’s model (readmission rate reduction: LACE, 39.2%; HOSPITAL, 43.4%; physician, 10.1%; p < 0.001). The HOSPITAL score provides a better prediction of readmission and has potential as a risk management tool for home care patients.

Economic Burden of Treatment-Resistant Depression Among Patients Hospitalized for Major Depressive Disorder in the United States

Objectives

This study aimed to evaluate hospital length of stay (LOS) and cost as well as readmission risk and the associated economic burden among patients hospitalized for treatment-resistant and non-treatment-resistant major depressive disorder.

Methods

Adult patients with a primary hospital discharge diagnosis of major depressive disorder were identified from the Premier Hospital Database (January 1, 2012-September 30, 2015). Patients were stratified into two cohorts: those whose hospital treatment was suggestive of treatment-resistant depression and those with non-treatment-resistant depression. Hospital LOS and cost during the initial admission and readmissions rates, LOS, and cost within the 6-month follow-up were compared between cohorts with a propensity score-matched sample.

Results

After matching, 45,066 patients were included in each cohort. For index hospitalizations, mean hospital LOS was longer (7.4 vs. 5.9 days, p<0.001) and mean hospital cost higher ($8,681 vs. $6,632, p<0.001) for patients with treatment-resistant depression vs. non-treatment-resistant depression. Rates for all-cause (24.4% vs. 20.0%, p<0.001), major depressive disorder-related (17.0% vs. 13.3%, p<0.001), and suicidal ideation/suicide attempt-related (12.8% vs. 9.5%, p<0.001) readmissions were higher for patients with treatment-resistant depression. Mean LOS and total hospital costs per patient for readmissions were also greater for patients with treatment-resistant depression vs. non-treatment-resistant depression. Correspondingly, the combined hospital cost (index hospitalization+all-cause readmissions) was greater for patients with treatment-resistant depression ($12,370 vs. $9,429, p<0.001).

Conclusions

Treatment-resistant depression was associated with substantial economic burden among patients hospitalized for major depressive disorder. More-effective treatment and care for this patient population may reduce the hospital burden of patients with treatment-resistant depression.

Effective mental health interventions to reduce hospital readmission rates: a systematic review

BACKGROUND

Hospitals in the United States are financially penalized for having a higher than expected thirty-day readmission ratio among patients initially hospitalized for heart failure, acute myocardial infarction (AMI), pneumonia, chronic obstructive pulmonary disease (COPD), coronary artery bypass graft (CABG) surgery, or hip and knee replacement. Patients hospitalized for these conditions that have comorbid mental health diagnoses or symptoms are at high risk for readmission.

METHODS

We conducted a systematic review to determine if interventions, that are specifically designed to assess or treat mental health symptoms, can effectively reduce risk of readmission following hospitalization for physical health conditions. We searched on PubMed and Google Scholar for peer-reviewed articles published between January 2010 and June 2018 that examined the impact of mental-health interventions on readmissions for physical conditions.

RESULTS

After screening 81 full text articles, we found eleven intervention studies, one meta-analysis, and one cross-sectional study that met our inclusion criteria. Only three of the intervention studies found significant differences in readmission rates between intervention and comparison groups. Each of these interventions targeted patients after discharge from the hospital. One of the interventions was a physical health telemonitoring and individual psychotherapy intervention for patients that were initially admitted for heart failure. The second intervention was individual and group psychotherapy sessions for patients who were initially admitted for AMI. The third intervention was a nurse-driven depression care management protocol for home care patients with depressive symptoms who were initially admitted for any physical health condition. The cross-sectional study showed that communities with a stronger, social-based public mental health infrastructure had significantly lower physical health readmission rates.

CONCLUSIONS

The literature identified in this review, appears to provide support for the use of mental health interventions after discharge as a mechanism for reducing physical health condition readmissions. Future research is needed to determine if these interventions can specifically reduce thirty-day readmissions for the six conditions linked to financial penalties.