Dynamic suicide topic modelling: Deriving population-specific, psychosocial and time-sensitive suicide risk variables from Electronic Health Record psychotherapy notes.

In the machine learning subfield of natural language processing, a topic model is a type of unsupervised method that is used to uncover abstract topics within a corpus of text. Dynamic topic modelling (DTM) is used for capturing change in these topics over time. The study deploys DTM on corpus of electronic health record psychotherapy notes. This retrospective study examines whether DTM helps distinguish closely matched patients that did and did not die by suicide. Cohort consists of United States Department of Veterans Affairs (VA) patients diagnosed with Posttraumatic Stress Disorder (PTSD) between 2004 and 2013. Each case (those who died by suicide during the year following diagnosis) was matched with five controls (those who remained alive) that shared psychotherapists and had similar suicide risk based on VA's suicide prediction algorithm. Cohort was restricted to patients who received psychotherapy for 9+ months after initial PTSD diagnoses (cases = 77; controls = 362). For cases, psychotherapy notes from diagnosis until death were examined. For controls, psychotherapy notes from diagnosis until matched case's death date were examined. A Python-based DTM algorithm was utilized. Derived topics identified population-specific themes, including PTSD, psychotherapy, medication, communication and relationships. Control topics changed significantly more over time than case topics. Topic differences highlighted engagement, expressivity and therapeutic alliance. This study strengthens groundwork for deriving population-specific, psychosocial and time-sensitive suicide risk variables.

National cross-sectional cohort study of the relationship between quality of mental healthcare and death by suicide.

BACKGROUND: Patient safety-based interventions aimed at lethal means restriction are effective at reducing death by suicide in inpatient mental health settings but are more challenging in the outpatient arena. As an alternative approach, we examined the association between quality of mental healthcare and suicide in a national healthcare system. METHODS: We calculated regional suicide rates for Department of Veterans Affairs (VA) Healthcare users from 2013 to 2017. To control for underlying variation in suicide risk in each of our 115 mental health referral regions (MHRRs), we calculated standardised rate ratios (SRRs) for VA users compared with the general population. We calculated quality metrics for outpatient mental healthcare in each MHRR using individual metrics as well as an Overall Quality Index. We assessed the correlation between quality metrics and suicide rates. RESULTS: Among the 115 VA MHRRs, the age-adjusted, sex-adjusted and race-adjusted annual suicide rates varied from 6.8 to 92.9 per 100 000 VA users, and the SRRs varied between 0.7 and 5.7. Mean regional-level adherence to each of our quality metrics ranged from a low of 7.7% for subspecialty care access to a high of 58.9% for care transitions. While there was substantial regional variation in quality, there was no correlation between an overall index of mental healthcare quality and SRR. CONCLUSION: There was no correlation between overall quality of outpatient mental healthcare and rates of suicide in a national healthcare system. Although it is possible that quality was not high enough anywhere to prevent suicide at the population level or that we were unable to adequately measure quality, this examination of core mental health services in a well-resourced system raises doubts that a quality-based approach alone can lower population-level suicide rates.

Improvements to PTSD quality metrics with natural language processing.

RATIONALE AIMS AND OBJECTIVES: As quality measurement becomes increasingly reliant on the availability of structured electronic medical record (EMR) data, clinicians are asked to perform documentation using tools that facilitate data capture. These tools may not be available, feasible, or acceptable in all clinical scenarios. Alternative methods of assessment, including natural language processing (NLP) of clinical notes, may improve the completeness of quality measurement in real-world practice. Our objective was to measure the quality of care for a set of evidence-based practices using structured EMR data alone, and then supplement those measures with additional data derived from NLP. METHOD: As a case example, we studied the quality of care for posttraumatic stress disorder (PTSD) in the United States Department of Veterans Affairs (VA) over a 20-year period. We measured two aspects of PTSD care, including delivery of evidence-based psychotherapy (EBP) and associated use of measurement-based care (MBC), using structured EMR data. We then recalculated these measures using additional data derived from NLP of clinical note text. RESULTS: There were 2 098 389 VA patients with a diagnosis of PTSD between 2000 and 2019, 72% (n = 1 515 345) of whom had not previously received EBP for PTSD and were treated after a 2015 mandate to document EBP using templates that generate structured EMR data. Using structured EMR data, we determined that 3.2% (n = 48 004) of those patients met our EBP for PTSD quality standard between 2015 and 2019, and 48.1% (n = 23 088) received associated MBC. With the addition of NLP-derived data, estimates increased to 4.1% (n = 62 789) and 58.0% (n = 36 435), respectively. CONCLUSION: Healthcare quality data can be significantly improved by supplementing structured EMR data with NLP-derived data. By using NLP, health systems may be able to fill the gaps in documentation when structured tools are not yet available or there are barriers to using them in clinical practice.