Psychodynamic psychotherapy is associated with sustained reduction in health care utilization and cost

Empirical examination of long-term and intensive psychodynamic psychotherapy for severely disturbed patients

Objectives This study examines the effectiveness and efficiency of intensive psychodynamic psychotherapy for severely impaired patients. Method: 104 patients in four public mental health centers underwent intensive psychodynamic psychotherapy. The number and duration of psychiatric hospitalizations were monitored for these patients from one year before therapy to eight years after. Several outcome variables were measured every six months, six times in total over two and a half years, using a longitudinal design. A multi-level analytic approach was applied to account for repeated measurements and missing data. Results: Significant improvement was found in all three symptomatic outcome measures (SCL-90, OQ-45, BDI) throughout treatment. The numbers of psychiatric hospitalizations and psychiatric hospitalization days decreased significantly from the level they were in the year before the start of psychodynamic treatment to three years after the start of treatment. These results were maintained for at least up to eight years. After capitalization, the overall cumulative 127.47-day decrease in hospitalization days equals savings of 115,850 NIS. The average cost of treatment after capitalization was 26,770 NIS. The insurer's estimated direct savings is 89,080 NIS (24,054 $). Conclusion: These findings support the hypothesis that psychodynamic psychotherapy is clinically effective and economically efficient for severely impaired patients.

Sick leave before, during, and after short-term outpatient psychotherapy: a cohort study on sick leave days and health care costs between behavioral and psychodynamic psychotherapies on anonymized claims data

BACKGROUND

Sick leave due to mental disorders poses a relevant societal and economic burden. Research on sick leave over a patient journey of individuals who received one of two treatment approaches - either behavioral (BT) or psychodynamic (PDT) psychotherapy - is scarce.

METHODS

We conducted a cohort study on anonymized German claims data for propensity-score matched patients who received short-term outpatient BT or PDT. We analyzed sick leave days and direct health care costs one year before, during, and one year after psychotherapy.

RESULTS

We analyzed data of patients who received BT and PDT, with N = 14 530 patients per group after matching. Patients showed sick leave days per person year of 33.66 and 35.05 days before, 35.99 and 39.74 days during, and 20.03 and 20.95 days after BT and PDT, respectively. Sick leave rates were overall higher in patients who received PDT. Both patient groups showed reductions of roughly 14 sick leave days per year, or 40%, from before to after therapy without a difference between BT and PDT (difference-in-difference [DiD] = -0.48, 95%-confidence interval [CI] -1.61 to 0.68). Same applies to direct health care costs which reduced in both groups by roughly 1800 EUR (DiD = 0, 95%-CI -158 to 157).

CONCLUSIONS

Results suggest similar reductions in sick leave days and direct health care costs from before to after BT and PDT. As sick leave is discussed to serve as an indicator of overall health and functioning in mental disorders, both treatments may have a similar positive impact on mental health.

Cost structure in specialist mental healthcare: what are the main drivers of the most expensive episodes?

BACKGROUND

Mental disorders are one of the costliest conditions to treat in Norway, and research into the costs of specialist mental healthcare are needed. The purpose of this article is to present a cost structure and to investigate the variables that have the greatest impact on high-cost episodes.

METHODS

Patient-level cost data and clinic information during 2018-2021 were analyzed (N = 180,220). Cost structure was examined using two accounting approaches. A generalized linear model was used to explain major cost drivers of the 1%, 5%, and 10% most expensive episodes, adjusting for patients' demographic characteristics [gender, age], clinical factors [length of stay (LOS), admission type, care type, diagnosis], and administrative information [number of planned consultations, first hospital visits, interval between two hospital episode].

RESULTS

One percent of episodes utilized 57% of total resources. Labor costs accounted for 87% of total costs. The more expensive an episode was, the greater the ratio of the inpatient (ward) cost was. Among the top-10%, 5%, and 1% most expensive groups, ward costs accounted for, respectively, 89%, 93%, and 99% of the total cost, whereas the overall average was 67%. Longer LOS, ambulatory services, surgical interventions, organic disorders, and schizophrenia were identified as the major cost drivers of the total cost, in general. In particular, LOS, ambulatory services, and schizophrenia were the factors that increased costs in expensive subgroups. The "first hospital visit" and "a very short hospital re-visit" were associated with a cost increase, whereas "the number of planned consultations" was associated with a cost decrease.

CONCLUSIONS

The specialist mental healthcare division has a unique cost structure. Given that resources are utilized intensively at the early stage of care, improving the initial flow of hospital care can contribute to efficient resource utilization. Our study found empirical evidence that planned outpatient consultations may be associated with a reduced health care burden in the long-term.

Systematic review of machine learning utilization within outpatient psychodynamic psychotherapy research

Introduction

Although outpatient psychodynamic psychotherapy is effective, there has been no improvement in treatment success in recent years. One way to improve psychodynamic treatment could be the use of machine learning to design treatments tailored to the individual patient's needs. In the context of psychotherapy, machine learning refers mainly to various statistical methods, which aim to predict outcomes (e.g., drop-out) of future patients as accurately as possible. We therefore searched various literature for all studies using machine learning in outpatient psychodynamic psychotherapy research to identify current trends and objectives.

Methods

For this systematic review, we applied the Preferred Reporting Items for systematic Reviews and Meta-Analyses Guidelines.

Results

In total, we found four studies that used machine learning in outpatient psychodynamic psychotherapy research. Three of these studies were published between 2019 and 2021.

Discussion

We conclude that machine learning has only recently made its way into outpatient psychodynamic psychotherapy research and researchers might not yet be aware of its possible uses. Therefore, we have listed a variety of perspectives on how machine learning could be used to increase treatment success of psychodynamic psychotherapies. In doing so, we hope to give new impetus to outpatient psychodynamic psychotherapy research on how to use machine learning to address previously unsolved problems.