Tag: Suicide prevention

MHRN III Pilot Project 1: Stakeholder Views on Implementation of Suicide Risk Prediction Models

Grant Details

Funder: NIMH

Grant Number: U19MH121738

Grant Period: 09/24/2019 – 6/30/2021

Narrative: Age-adjusted suicide rates have been increasing in the U.S. over the past two decades. In 2017, more than 47,000 Americans died of suicide. Health care visits represent opportunities for suicide prevention because most individuals make an outpatient health care visit within a year of their suicide death and almost half have a visit within a month of their death. However, suicide risk is not always easily recognizable to clinicians—traditional clinical prediction is hardly better than chance. Predictive modeling that identifies patterns in “big data” from administrative and electronic health records has proven superior to clinical suicide risk prediction and routinely used suicide screening instruments. While predictive modeling holds promise for suicide prevention, how models should be implemented in routine clinical practice and the contextual factors that influence their use are understudied. The potential benefits of any risk prediction model, including those designed to identify suicide risks, are dependent on making sure that the models are deployed in a manner that does not harm patients, supports clinical care management, and is sustainable for health care delivery systems. We propose a pre-implementation pilot study in three settings, using one-on-one, in-depth interviews to explore health system administrators’, clinicians’, and patients’ expectations, experiences with, concerns, and suggestions for the early use of suicide risk prediction models. In the first setting, health system administrators are still considering what might be the best implementation approach. Interviews will help us understand how various stakeholder expectations match what is actually occurring in the two other settings where small pilot studies will be in process. One of these settings is planning outreach to high-risk patients independent of health care visits while the other is planning delivery of risk scores at the point of care. By studying different implementation strategies, we can compare relative advantages and disadvantages. We are particularly interested in effects on clinical workflows, clinician-patient relationships, and patient experiences. While there is an emerging literature supporting the promise of predictive models in health care, implementation factors and patient impacts have been largely ignored. Yet decisions regarding design and modeling methods and implementation processes should be driven by stakeholder requirements. Results of this pilot study will have important clinical implications and will not only inform large-scale implementation of suicide risk prediction models in health systems across the country but will also inform development of future risk prediction models and associated care processes tailored to stakeholders needs more generally (not limited to suicide risk). The long-term goals of this pilot project are to inform ongoing health system-level efforts to reduce suicide prevalence and prevent suicides by optimizing the use of suicide risk prediction tools.

  • Lead Site:
    • Overall PI: KPNW (Bobbi Jo Yarborough)
  • Participating Sites/Subcontractors:
    • HPI (site project lead Rebecca Rossom)
    • KPWA (site project lead Julie Richards; site PI Greg Simon)
  • Funder Contacts
    • Science Officer: Susan Azrin
    • Program Official: Michael Freed
    • Grants Management Official: Julie Bergerud

Documents

Funding Announcement

Notice of Award

Personnel Contact List

Current Status

We have completed and analyzed interviews with 10 health care administrators, 30 clinicians in behavioral health departments, and 62 patients across three health systems.

Summary of Findings

Administrators and clinicians

  • Use of a suicide risk prediction model and two differing implementation approaches were acceptable.
  • Clinicians desired opportunities for input on implementation decision-making.
  • They wanted to know how this manner of risk identification enhanced existing suicide prevention efforts.
  • They wanted additional training on how the models determined risk and why some patients appeared at risk while others do not.
  • Clinicians were concerned about lack of suicide prevention resources for newly identified patients.
  • They wanted clear procedures for situations when they could not reach patients or when patients remained at-risk over a sustained period.
  • They would like consolidated suicide risk information in a dedicated module in the EHR to increase efficiency.

Patients

  • Patients were generally supportive of suicide risk prediction models derived from EHR data.
  • Concerns included: 1) apprehension about inducing anxiety and suicidal thoughts, or 2) triggering coercive treatment, particularly among those who reported prior negative experiences seeking mental health care.
  • Participants engaged in mental health care or case management expected to be asked about suicide risk and largely appreciated suicide risk conversations
  • Patients preferred conversations to come from clinicians comfortable discussing suicidality.

Publications

Yarborough BJH, Stumbo SP. Patient perspectives on acceptability of, and implementation preferences for, use of electronic health records and machine learning to identify suicide risk. Gen Hosp Psychiatry. 2021 May-Jun;70:31-37. doi: 10.1016/j.genhosppsych.2021.02.008.

Yarborough BJH, Stumbo SP, Schneider JL, Richards JE, Hooker SA, Rossom RC . Patient expectations of and experiences with a suicide risk identification algorithm in clinical practice. BMC Psychiatry. 2022 Jul 23;22(1):494. doi: 10.1186/s12888-022-04129-1 .

MHRN III Infrastructure: Methods Core

Grant Details

Funder: NIMH

Grant Number: U19MH121738

Grant Period: 09/23/2019 – 06/30/2020

Narrative: The Methods Core will include an Informatics Unit, led by Drs. Gregory Simon and Christine Stewart, and a Scientific Analysis Unit, led by Drs. Susan Shortreed and Patrick Heagerty. The Informatics Unit will continue highly successful work over the past 8 years, supporting routine data quality assessment and descriptive analyses of diagnosis and treatment patterns across all participating health systems. New work will include development of tools and resources to assess and minimize privacy risks when sharing sensitive health data for research and development of specific new data areas (perinatal mental health and prenatal exposures, expanded list of patient-reported outcomes, and assessments of social determinants of health). The Informatics Unit will provide consultation to all MHRN core and affiliated projects and share all resources with other researchers and health systems via MHRN’s public repository of specifications, code lists, and analytic code. The Scientific Analysis Unit will support to all MHRN core and affiliated projects via project-specific consultation and development of a learning community of analysts and biostatisticians across MHRN research centers. This Unit will also focus on development and dissemination of analytic methods in two areas directly relevant to MHRN research. Work on evaluating adaptive treatment strategies will build on Dr. Shortreed’s recently funded methods grant to evaluate and disseminate methods for using health system data to tailor treatments for individuals with more chronic or severe mental health conditions, focusing on assessing treatment effects when treatments are adjusted or switched according to previous treatment failures or adverse effects. Work on stakeholder-driven predictive analytics will build on MHRN’s development of accurate suicide risk prediction models, focusing on matching specific study designs and model development methods with stakeholder priorities and implementation constraints.

Lead Site: KPWA (PI Greg Simon)

Participating Sites: University of Washington (Site PI Patrick Heagerty) 

  • Funder Contacts
    • Science Officer: Susan Azrin
    • Program Official: Michael Freed
    • Grants Management Official: Jackie Chia

Documents & Reports

Submitted Proposal

Specific Aims

Research Plan

Notice of Award

Personnel Contact List

Publications

Evaluating Zero Suicide Care Improvement Programs in MHRN Health Systems

Grant Details

Title: An Evaluation of the National Zero Suicide Model Across Learning Healthcare Systems

Funder: NIMH

Grant Number: 1U01MH114087

Grant Period: 08/03/2017 – 05/31/2022

Narrative: Health systems at six participating sites have all committed to developing and implementing various components of a National Zero Suicide Model (NZSM), originally developed at the lead site for this study, Henry Ford Health System (HFHS).  Each health system will decide which components to implement at their respective site.  This study will develop metrics to measure fidelity and outcomes for the NZSM components implemented in each system using EHR and insurance claims data.  The project will then use these metrics to conduct fidelity and outcome evaluation of the various NZSM approaches in each system using an Interrupted Time Series Design.

Short-term project objectives:

We seek to accomplish three specific aims:

  1. Collaborate with health system leaders across sites to develop EHR metrics to measure specific quality improvement targets and care processes tailored to local NZSM implementation.
  2. Examine the fidelity of the specific NZSM care processes implemented in each system.
  3. Investigate suicide attempt and mortality outcomes within and across NZSM system models.

Long-term project objectives:

Learnings from this study will be immediately available on the Zero Suicide and MHRN websites, shared directly with SAMHSA and NIMH (thru the MHRN), and disseminated broadly to health systems via Zero Suicide Training Academies well before published data are available. As such, our goal is rapid dissemination and translation to practice, as opposed to the standard research-to-practice model – which the NIH and others estimate can take 17 years.

  • Lead Site:
    • Overall PI: HFHS Brian Ahmedani
  • Participating Sites/Subcontractors:
    • KPWA (site PI Greg Simon)
    • KPCO (site PI Jennifer Boggs)
    • KPNW (site PI Greg Clarke)
  • Funder Contacts
    • Science Officer: Susan Azrin
    • Program Official: Michael Freed
    • Grants Management Official: Julie Bergerud

Documents & Reports

Funding Announcement

Personnel Contact List

Publications

Manuscripts in process

ZS manuscript tracker: https://airtable.com/shr7wfbafq5c1rwTY

MHRN manuscript proposal form: https://airtable.com/shrD81CbLqaRrF8ga

MHRN III Infrastructure: Administrative Core

Grant Details

Funder: NIMH

Grant Number: U19MH121738

Grant Period: 09/23/2019 – 06/30/2020

  • Narrative:​ Practice-based research has the potential to dramatically improve the speed, efficiency, relevance, and impact of mental health clinical and services research.  Mental Health Research Network (MHRN) III will include 14 research centers embedded in health systems serving a combined population of over 25 million patients in 16 states.  MHRN infrastructure will be enhanced to support a next-generation practice-based network, including:
    • Increased engagement of patients, health system leaders, and other stakeholders in network governance
    • An expanded public, open-source library of software tools and other technical resources
    • More formal processes for conducting feasibility pilot projects and rapid response to stakeholder queries
    • Expanded outreach to external stakeholders and research partners
  • Lead Site: KPWHRI
    • Overall PI: Greg Simon
  • Participating Sites/Subcontractors:
    • Baylor Scott & White – Site PI: Katherine Sanchez
    • Cornell University – Site PI: Jyotishman Pathak
    • Essentia Institute of Rural Health – Site PI: Stephen Waring
    • Georgia State University – Site PI: Ashli Owen-Smith
    • Harvard Pilgrim – Site PI: Christine Lu
    • HealthPartners – Site PI: Rebecca Rossom
    • Henry Ford Health System – Site PI Brian Ahmedani
    • KP Colorado – Site PI: Arne Beck
    • KP Georgia – Site PI Courtney McCracken
    • KP Hawaii – Site PI: Yihe Daida
    • KP Northern California – Site PI: Stacy Sterling
    • KP Northwest – Site PI: Frances Lynch
    • KP Southern California – Site PI: Karen Coleman
    • PalAlto Medical Foundation – Site PI: Ellis Dillon
  • Funder Contacts
    • Science Officer: Susan Azrin
    • Program Official: Michael Freed
    • Grants Management Official: Jackie Chia
  • Awarded Budget (Total Cost)
    • Year 1: $2,220,745
    • Year 2: $2,052,966
    • Year 3: $2,035,335
    • Year 4: $2,000,066
    • Year 5: $1,967,876

Documents & Reports

  • IRB Review
    • KPWA IRBnet file: [ 1475733 ]
    • KPWA IRB is single IRB reviewing for BSWH, HPHC, HPI, KPNC, KPNW, and KPSC.
    • EIRH, HFHS, KPCO, (KPGA?), KPHI, and PAMF IRB determination that work is exempt.
    • GSU and UW IRB determination that work is research not involving human subjects.

Personnel Contact List

Funded feasibility pilot projects

Publications

MRHN III Supplemental Project: Effect of Initiating Buprenorphine on Suicidal Behavior

Grant Details

Title: Buprenorphine Effect on Suicidal Behavior

Funder: NIMH

Grant Number: U19MH121738-02S2  (supplement to main MHRN cooperative agreement)

Grant Period: 9/17/2020 – 8/31/2022

Narrative: This large observational study will evaluate the effects of initiating buprenorphine treatment on subsequent suicidal behavior among people with opioid use disorder, including those with and without co-occurring mental health conditions or other known risk factors for suicidal behavior. We will use comprehensive health records data from four large health systems serving a combined member/patient population of approximately 11 million. Analyses will examine the overall effect of buprenorphine treatment on subsequent suicide attempts or death, heterogeneity of effects in patient subgroups, and specificity of effects to buprenorphine vs other medications.

We will be using previously developed suicide risk prediction tools to compare the outcomes of individuals who do and do not use buprenorphine with similar baseline suicide risk.

  • Lead Site: KPWA
    • Overall PI: Greg Simon
  • Participating Sites/Subcontractors:
    • KPNC – Site PI: Cynthia Campbell
    • KPSC – Site PI: Rulin Hechter
    • Henry Ford – Site PI: Brian Ahmedani
  • Funder Contacts:
    • Science Officer: Susan Azrin
    • Program Official: Michael Freed
    • Grants Management Official: Julie Bergerud
  • Awarded Budget (Total Cost):
    • Year 1: $514,616
    • Year 2: $274,321

Documents

Funding Announcement: PA-18-591

Notice of Award

Personnel Contact List

Human Subjects: YES

  • IRB Review:
    • KPWA is single IRB reviewing for KPWA, KPNC, and KPSC – Approved waiver of consent for use of records data
    • Henry Ford determined to be exempt
    • KPWA IRBnet File: [1649129]

Clinical Trial: NO

Current status

Exploratory analyses (in preparation for extraction of data at each site) have examined availability and quality of data regarding opioid medication use, availability and quality of data regarding injury and poisoning events, and types of visits occurring prior to initial buprenorphine prescriptions.  These analyses are informing refinements to research design and data specifications.  Final data extraction will occur during in October 2022 with analyses complete in early 2023.

Summary of findings

Not yet available

Publications

None

Suicide Prevention Interest Group

Goals / Mission

  • Variations in patterns of heathcare and other factors before suicide attempt and suicide death
  • Implementing standardized suicide risk screening and assessment
  • Low-intensity interventions for prevention of suicide attempt
  • Provider- and System-level interventions to prevent suicidal behavior

Research Priorities

The Suicide Prevention Interest Group is focused on a series of high priority topics aligning with national recommendations released in the Prioritized Research Agenda for Suicide Prevention. Specifically, our group seeks to identify clinical and health services factors that allow us to better predict suicide attempt and death as well as engage experts, stakeholders, and patients to develop and test large-scale suicide prevention interventions within and across health system settings.

Contact:

Brian K. Ahmedani, PhD

Director, Center for Health Policy & Health Services Research

Director of Research, Behavioral Health Services

Senior Scientist Henry Ford Health System
1 Ford Place
Detroit, MI 48202

Phone: 313-874-5454

Email: BAHMEDA1@hfhs.org

Computational Modeling of Suicide Risk

Project Name:
Computational Modeling of Suicide Risk
Principal Investigator:
Gregory Simon MD MPH
Principal Investigator Contact Information: 
gregory.e.simon@kp.org
Principal Investigator Institution:
KP Washington Health Research Institute
Funder:
National Institute of Mental Health (NIMH)
Funding Period:
7/1/2017 to 6/30/2019
Abstract:
A previous supplement to the MHRN cooperative agreement supported development of a population-based suicide risk calculator, predicting risk of suicide attempt or suicide death following an outpatient visit using both responses to PHQ9 item 9 and discrete data extracted from health system electronic health records.  Using a database of approximately 20 million visits by 3 million patients aged 13 and over, we developed and validated machine learning logistic regression models predicting risk of suicide attempt and suicide death within 90 days of an outpatient visit – either a visit to a specialty mental provider or a primary care visit in which a mental health diagnosis was recorded.  Potential predictors included demographic and clinical data extracted from health system records for the 5 years prior to each visit: prior suicidal behavior, mental health and substance use diagnoses, general medical diagnoses, prescriptions for psychiatric medications, inpatient or emergency department mental health care, and responses to routinely administered PHQ9 depression questionnaires.  Models were developed in a 65% random sample of visits and validated in the remaining 35%.  Variable selection models considered 150 discrete predictors and 164 potential interactions.  In the validation sample, the 5% of mental health specialty visits with highest risk scores accounted for 43% of subsequent suicide attempts and 47% of suicide deaths.  Areas under the receiver operating characteristic curves (AUCs) for prediction of suicide attempt and suicide death were 0.85 and 0.86.  In the validation sample, the 5% of primary care visits with highest risk scores accounted for 48% of subsequent suicide attempts and 43% of suicide deaths.  AUCs for prediction of suicide attempt and suicide death were 0.85 and 0.83.

While these models represent a substantial advance over existing risk prediction or risk stratification tools, we identify several significant limitations.  Fixed limits of our computational methods (penalized LASSO logistic regression in the R computing environment) forced us to limit both our sample size and the number of potential predictors and interaction terms.  Those methods also limit ability to appropriate account for clustering of observations within patients and account for the sparse and skewed distributions of predictor data.  Finally, we now recognize the need to extend these methods to predict risk following acute-care (inpatient and emergency department) encounters. We now propose a next stage of work to address these limitations.  Specific aims of this next stage include: Expand and enhance the risk prediction dataset to: include larger numbers of observations with data regarding self-reported suicidal ideation (PHQ9 Item 9), include additional encounters and events following the transition from ICD9 to ICD10 diagnoses, and allow more detailed consideration of the timing of predictor events (diagnoses, encounters, prescription fills)Expand sampling to include emergency department and inpatient encounters. Evaluate alternative modeling approaches, including classification- or tree-based approaches such as Classification and Regression Trees (CART), Mixed Effects Regression Trees (MERT), and Random Forest. Rapidly disseminate all methods, tools and results to a wide range of stakeholders including health systems, researchers, and EHR vendors.
Grant Number:
MH 092201 (supplement)
Participating Sites:
Kaiser Permanente Washington
Kaiser Permanente Northwest
Kaiser Permanente Southern California
Kaiser Permanente Hawaii
HealthPartners
Henry Ford Health System:
Kaiser Permanente Colorado
Investigators:
Gregory Simon MD MPH
Susan Shortreed PhD
Yates Coley PhD
Frances Lynch PhD
Jean Lawrence ScD
Beth Waitzfelder PhD
Rebecca Rossom MD MS
Brian Ahmedani PhD
Arne Beck PhD
Major GoalsExpand and enhance the risk prediction dataset to: include larger numbers of observations with data regarding self-reported suicidal ideation (PHQ9 Item 9), include additional encounters and events following the transition from ICD9 to ICD10 diagnoses, and allow more detailed consideration of the timing of predictor events (diagnoses, encounters, prescription fills)Expand sampling to include emergency department and inpatient encounters. Evaluate alternative modeling approaches, including classification- or tree-based approaches such as Classification and Regression Trees (CART), Mixed Effects Regression Trees (MERT), and Random Forest. Rapidly disseminate all methods, tools and results to a wide range of stakeholders including health systems, researchers, and EHR vendors.
Description of study sample:
Expected to include approximately 30 million encounters by approximately 4 million members in seven health systems.
Current Status:
As of 7/1/2019:  Data harvest, data quality control, and preliminary analyses are complete for the mental health and primary care visit cohorts. Data harvest and data quality control are underway for inpatient and emergency department cohorts.
Study Registration:
N/A
Publications:
N/A
Resources:
N/A
Lessons Learned:
Preliminary analyses indicate that: Models developed prior to October 2015 to predict ICD-9 self-harm diagnoses appear to perform as well when used after October 2015 to predict ICD-10 self-harm diagnoses. More complex ensemble-based model development methods (such as Random Forests) do not appear superior to parametric (such as penalized logistic) methods when predictors are primarily dichotomous. Inclusion of multiple visits per patient does not appear to contribute to over-fitting with parametric model development methods.
What’s next?
N/A

Developing Tools to Evaluate the Impact of Safety Planning and Lethal Means Assessment on Suicide Outcomes

Project Name:
Developing Tools to Evaluate the Impact of Safety Planning and Lethal Means Assessment on Suicide Outcomes
Principal Investigator:
Brian Ahmedani PhD
Jennifer M Boggs, MSW, PhD (Primary Contact)
Principal Investigator Contact Information: 
BAHMEDA1@hfhs.org        
Principal Investigator Institution:
Henry Ford Health System
Funder:
NIMH
Funding Period:
07/14/2018 – 7/31/2020
Abstract:
This timely supplement would support our goals for the current award: An Evaluation of the National Zero Suicide Model Across Learning Healthcare Systems (U01MH114087) by capitalizing on a natural experiment, the planned the national roll-out of safety planning templates in behavioral health departments across five Kaiser Permanente regions and Henry Ford Health System in 2019. Safety planning is a highly recommended practice within the Zero Suicide (ZS) framework, but little is known about the effectiveness of the individual elements that can make up a safety plan, such as lethal means assessment, identification of supportive contacts, coping skills, warning signs, and sources of distraction. The current Zero Suicide award proposes to examine the impact of safety planning and lethal means assessment using a stepped-wedged interrupted time-series (ITS) approach, measuring each as a binary variable (e.g. safety planning did or did not occur).  The ITS approach requires that some sites implement safety planning (intervention sites for safety planning), while others do not (control sites for safety planning). The proposed ITS approach is now problematic without further work for two reasons: 1) All Kaiser Permanente sites and Henry Ford have decided to uniformly implement safety planning around the same time, therefore there are no control sites 2) Without control sites, metrics that can accurately measure variation in safety planning/lethal means assessment at baseline and then longitudinally thereafter would enable our evaluation to take place, but all of the documentation lives in text-based clinical narratives. In working with our health system leads on the development of Zero Suicide metrics, we have been informed that the rate for safety planning and lethal means assessment at baseline is not zero, but the actual rate is unknown. This supplement will support development of new metrics using Natural Language Processing to determine baseline rates, from which, we can quantify the change in safety planning and lethal means assessment practice longitudinally after implementation of new safety planning templates using our Zero Suicide main award. Furthermore, we propose to take advantage of the newly implemented templates to address an important mediator of the effect of safety planning on suicide outcomes, the impact of fidelity to the new templates, which we define as quality, completeness, and level of integration with ongoing care. We propose the following three specific aims for this supplemental work: 1) Identify key terms for safety planning and lethal means assessment 1.) Develop Natural Language Processing (NLP) metrics to assess the occurrence of safety planning and lethal means assessment at three Zero Suicide sites 2) Implement NLP queries for identification of safety planning and lethal means assessment and measure baseline rates 3) Upon implementation of electronic safety planning templates in medical records, develop and implement metrics using NLP for assessing fidelity (completeness, quality, integration with care) to safety planning templates.   
Grant Number:
3U01MH114087-02S1 REVISED
Participating Sites:
Henry Ford Health System
Kaiser Permanente Colorado  
Kaiser Permanente Northwest
Investigators:
Jennifer M Boggs, MSW, PhD
BobbiJo Yarborough, PsyD
Arne Beck, PhD
Major Goals: Develop Natural Language Processing (NLP) metrics to assess the occurrence of safety planning and lethal means assessment at three Zero Suicide sites. Implement NLP queries for identification of safety planning and lethal means assessment at three Zero Suicide sites and measure baseline rates. Upon implementation of electronic safety planning templates in medical records, develop and implement metrics using NLP for assessing fidelity (completeness, quality, integration with care) to safety planning templates.
Description of study sample:
Our study sample will include patients at risk for suicide who were members of the Kaiser Permanente Colorado (KPCO), Kaiser Permanente Northwest (KPNW) or Henry Ford Health System (HFHS) for at least 1 year. 
For all aims, patients must meet one (or both) of these two criteria defining an index event between 2013 – 2017:endorsement of the suicide question (item 9) of the PHQ9 depression questionnaire suicide attempt coded in the emergency department/hospital
Current Status:
09/26/2018 – The project was recently funded and is in the process of completing regulatory applications at all of the sites.
Study Registration:
N/A
Publications:
N/A
Resources:
N/A
Lessons Learned:
N/A
What’s next?
We have developed an initial program to identify the cohort at each site and will distribute this when all sites have achieved regulatory approvals, hopefully by October 1, 2018.  Chart reviewer training documents have been developed and distributed to all sites.  A chart reviewer training has been scheduled for early October.  We hope to complete the phase 1 chart review before the end of 2018.

Effects of Medical Products on Suicidal Ideation and Behavior

Project Name:
Effects of Medical Products on Suicidal Ideation and Behavior
Principal Investigator:
Gregory Simon, MD, MPH
Principal Investigator Contact Information: 
gregory.e.simon@kp.org
Principal Investigator Institution:
KP Washington Health Research Institute
Funder:
Food and Drug Administration (FDA)
Funding Period:
9/30/2018 to 9/30/2021
Abstract:
We propose a comprehensive program of infrastructure development and methods development to support future generation of real-world evidence addressing these critical gaps.  The project team will include health systems and embedded research organizations with deep expertise in stakeholder engagement, medical informatics, data science, clinical epidemiology, biostatistics, pragmatic clinical trial methods, implementation science, and innovations in care delivery. Specific Tasks include: Augment the existing FDA Sentinel Initiative data infrastructure to support study of severe mental illness, suicidal ideation, and suicidal behavior. Evaluate and improve generalizability of models predicting suicidal behavior for use in future observational research and pragmatic trials. This program will be embedded in 4 integrated health systems serving a combined population of approximately 10 million members.  This work will be conducted in collaboration with health system and patient/family stakeholders, to assure that methods and evidence developed will actually address real-world questions. This infrastructure and methods development will enable a robust program of research regarding the effects of medical products on suicidal ideation and behavior, including: Scalable and re-usable methods to assess suicidal ideation and behavior as an adverse effect of existing products. Scalable and re-usable methods to assess therapeutic effects of existing products for reducing suicidal ideation and behaviorScalable and re-usable methods to rapidly evaluate possible therapeutic and adverse effects of new medical products on suicidal ideation and behavior. Large pragmatic trials to evaluate therapeutic effects of promising new product(s) on suicidal behavior
Grant Number:
N/A
Participating Sites:
Kaiser Permanente Washington
Harvard Pilgrim Healthcare
Kaiser Permanente Northern California
Kaiser Permanente Southern California
Henry Ford Health System                
Investigators:
Gregory Simon MD MPH
Susan Shortreed PhD
Yates Coley PhD
Richard Platt MD MS
Jeffrey Brown PhD
Darren Toh ScD
Jessica Young PhD
Stacy Sterling PhD
Karen Coleman PhD
Jean Lawrence ScD
Brian Ahmedani PhD
Major Goals Augment the existing FDA Sentinel Initiative data infrastructure to support study of severe mental illness, suicidal ideation, and suicidal behavior. Evaluate and improve generalizability of models predicting suicidal behavior for use in future observational research and pragmatic trials.
Description of study sample:
Various analyses are using data regarding approximately 4.5 million members of participating health systems.
Current Status:
Completed data infrastructure work includes:
– A toolkit to assess re-identification risk when sharing data derived from healthcare records:
– More timely updating of mortality data in health system research data warehouses.
– Regular reporting of availability and quality of patient-reported outcome data in health system research data warehouses.
Analyses are complete regarding:
– Value of more detailed data representation and more complex modeling methods for prediction of suicidal behavior.
– Accuracy of ICD-10-CM encounter diagnoses for identifying self-harm events.
– Value of data typically only available from electronic health records for prediction of suicidal behavior.
Study Registration:
N/A
Publications:
Simon GE, Shortreed SM, Boggs JM, Clarke GN, Rossom RC, Richards JE, Beck A, Ahmedani BK, Coleman KJ, Bhakta B, Stewart CC, Sterling S, Schoenbaum M, Coley RY, Stone M, Mosholder AD, Yaseen ZS. Accuracy of ICD-10-CM encounter diagnoses from health records for identifying self-harm events. J Am Med Inform Assoc. 2022 Aug 26:ocac144. doi: 10.1093/jamia/ocac144.
Resources:
N/A
Lessons Learned:
For prediction of suicidal behavior following outpatient mental health visits, more detailed temporal representation and more complex model development methods (random forest or neural networks vs. penalized logistic regression) do not meaningfully improve prediction accuracy.
When using prediction models to account for confounding by indication in observational studies of medication effects on suicidal behavior, random forest models may be slightly – but not meaningfully – superior to penalized logistic regression.
When using health records data to predict suicidal behavior, additional data available only from electronic health records (race, ethnicity, patient-reported outcome results) do not significantly improve prediction over data typically available from insurance claims.
What’s next?
Additional analyses will examine:
– Similarities and differences in prediction of opioid vs. other overdoses
– Similarities and differences in prediction of self-harm vs. accidental overdoses
– Changes in accuracy of suicide risk prediction models with health system implementation of Zero Suicide care improvement programs.

Evaluating the Impact of Changes in Opioid Prescribing across Health Systems Implementing Zero Suicide

Project Name:
Evaluating the Impact of Changes in Opioid Prescribing across Health Systems Implementing Zero Suicide
Principal Investigator:
Brian Ahmedani PhD (Contact PI)
Principal Investigator Contact Information: 
BAHMEDA1@hfhs.org         
Principal Investigator Institution:
Henry Ford Health System
Funder:
NIMH
Funding Period:
09/08/2018 – 09/07/2019
Abstract:
Suicide is a major public health concern – it is the 10th leading cause of death and number one cause of injury-related death in the United States (US). Suicide rates have risen over 25% in the last 15 years.  In parallel, the nation is struggling with an opioid epidemic.  Opioid prescribing, heroin use, and opioid-related overdose deaths have risen substantially.  Approximately 15% of all suicide deaths are due to drug overdose, and prescription opioids specifically, are commonly used among people who attempt suicide.  Health systems across the country have made decisions to tackle both of these public health crises – implementing policies to dramatically reduce opioid prescribing as well as clinical processes within the Zero Suicide model to improve suicide prevention for their patients. The parent award for this supplement is focused on evaluation of Zero Suicide implementation, including fidelity to each of these clinical processes and suicide outcomes, across 6 large, diverse Mental Health Research Network-affiliated Learning Healthcare Systems providing healthcare for over 9 million individuals each year. Given the overlap, significant reductions in opioid prescribing as part of newly implemented policies should lead to a reduction in the availability of opioids.  These reductions may result in a public-health level means reduction approach to reduce suicide.  Means reduction is among the interventions recommended within Zero Suicide.  The concurrent implementation of these new opioid prescribing policies in the context of implementation of Zero Suicide allows the opportunity to evaluate how changes in opioid prescribing impacts suicide outcomes in health care. This supplement project seeks to accomplish three specific aims: 1) Evaluate changes in opioid prescribing patterns during the period of NZSM implementation across health systems, 2) Investigate whether changes in opioid prescribing patterns reduce suicide attempt and mortality, and 3) Investigate whether changes in opioid prescribing patterns reduce opioid-related suicide attempt and mortality poisonings. Overall, we propose to use an Interrupted Time Series Design, consistent with the parent award, to measure changes in prescribing patterns and suicide outcomes.
Grant Number:
U01MH114087-02S2
Participating Sites:
Henry Ford Health System
Kaiser Permanente Washington
Kaiser Permanente Colorado  
Kaiser Permanente Northern California
Kaiser Permanente Northwest
Kaiser Permanente Southern California
Investigators:
Gregory Simon, MD, MPH (co-PI)
BobbiJo Yarborough, PsyD
Stacy Sterling, DrPH
Karen Coleman, PhD
Arne Beck, PhD
Major Goals: Evaluate changes in opioid prescribing patterns during the period of NZSM implementation across health systems. Investigate whether changes in opioid prescribing patterns reduce suicide attempt and mortality. Investigate whether changes in opioid prescribing patterns reduce opioid-related suicide attempt and mortality poisonings.
Description of study sample:
N/A
Current Status:
06/26/2019 – We have finalized the study protocol and methods, including finalizing the data metrics.  We have drafted the specifications for the program to extract the electronic health record data from the participating sites.
Study Registration:
N/A
Publications:
N/A
Resources:
N/A
Lessons Learned:
N/A
What’s next?
We will finalize the program specifications, write/test/distribute the program, and collect the final data for analyses