Estimating local need for mental healthcare to inform fair resource allocation in the NHS in England: cross-sectional analysis of national administrative data linked at person level

Outcomes for university students following emergency care presentation for deliberate self-harm: a retrospective observational study of emergency departments in England for 2017/2018

OBJECTIVES

Identify university-aged students and contrast their healthcare provision and outcomes with other patients in the same age group attending emergency departments for deliberate self-harm.

DESIGN

Retrospective cross-sectional observational study.

SETTING

Patients visiting 129 public hospital emergency departments across England between April 2017 and March 2018.

PARTICIPANTS

14 074 patients aged 18-23 visiting emergency departments for conditions linked to deliberate self-harm, 1016 of which were identified as university-aged students.

OUTCOME MEASURES

We study various outcomes across the entire patient pathway in the emergency department: waiting time to initial assessment on arrival at the emergency department, count of investigations delivered, discharge destination (patients refusing treatment or leave before being seen, referred to another provider or admitted to inpatient care, discharged with no follow-up) and unplanned follow-up visit within 7 days.

RESULTS

We find a statistically significant difference of 0.262 (-0.491 to -0.0327) less investigations delivered to students compared with non-students (about 8% compared with the baseline number of investigations for non-students). Stratified analyses reveal that this difference is concentrated among students visiting the emergency department outside of regular working hours (-0.485 (-0.850 to -0.120)) and students visiting for repeated deliberate self-harm episodes (-0.881 (-1.510 to -0.252)). Unplanned reattendance within 7 days is lower among students visiting emergency departments during out of hours (-0.0306 (-0.0576 to -0.00363)), while students arriving by ambulance are less likely to be referred to another provider (-0.0708 (-0.140 to -0.00182)) compared with non-students.

CONCLUSIONS

We find evidence of less-intense investigations being delivered to patients aged 18-23 identified as students compared with non-students visiting emergency departments after an episode of deliberate self-harm. Given the high risk of suicide attempts after episodes of deliberate self-harm among students, our findings may highlight the need for more focused interventions on this group of patients.

Use of past care markers in risk-adjustment: accounting for systematic differences across providers

Risk-adjustment models are used to predict the cost of care for patients based on their observable characteristics, and to derive efficient and equitable budgets based on weighted capitation. Markers based on past care contacts can improve model fit, but their coefficients may be affected by provider variations in diagnostic, treatment and reporting quality. This is problematic when distinguishing need and supply influences on costs is required.We examine the extent of this bias in the national formula for mental health care using administrative records for 43.7 million adults registered with 7746 GP practices in England in 2015. We also illustrate a method to control for provider effects.A linear regression containing a rich set of individual, GP practice and area characteristics, and fixed effects for local health organisations, had goodness-of-fit equal to R² = 0.007 at person level and R² = 0.720 at GP practice level. The addition of past care markers changed substantially the coefficients on the other variables and increased the goodness-of-fit to R² = 0.275 at person level and R² = 0.815 at GP practice level. The further inclusion of provider effects affected the coefficients on GP practice and area variables and on local health organisation fixed effects, increasing goodness-of-fit at GP practice level to R² = 0.848.With adequate supply controls, it is possible to estimate coefficients on past care markers that are stable and unbiased. Nonetheless, inconsistent reporting may affect need predictions and penalise populations served by underreporting providers.

Applications of artificial intelligence to improve patient flow on mental health inpatient units - Narrative literature review

BACKGROUND

Despite a growing body of research into both Artificial intelligence and mental health inpatient flow issues, few studies adequately combine the two. This review summarises findings in the fields of AI in psychiatry and patient flow from the past 5 years, finds links and identifies gaps for future research.

METHODS

The OVID database was used to access Embase and Medline. Top journals such as JAMA, Nature and The Lancet were screened for other relevant studies. Selection bias was limited by strict inclusion and exclusion criteria.

RESEARCH

3,675 papers were identified in March 2020, of which a limited number focused on AI for mental health unit patient flow. After initial screening, 323 were selected and 83 were subsequently analysed. The literature review revealed a wide range of applications with three main themes: diagnosis (33%), prognosis (39%) and treatment (28%). The main themes that emerged from AI in patient flow studies were: readmissions (41%), resource allocation (44%) and limitations (91%). The review extrapolates those solutions and suggests how they could potentially improve patient flow on mental health units, along with challenges and limitations they could face.

CONCLUSION

Research widely addresses potential uses of AI in mental health, with some focused on its applicability in psychiatric inpatients units, however research rarely discusses improvements in patient flow. Studies investigated various uses of AI to improve patient flow across specialities. This review highlights a gap in research and the unique research opportunity it presents.