development of an eHR registry for implementing collaborative care of Depression An Information Technology tool to support implementation Society for Implementation research collaboration Ian M. Bennett 9/6/2017

University of Washington Family Medicine Partners University of Washington Family Medicine Ian Bennett Psychiatry and Behavioral Sciences & AIMS Center Jurgen Unutzer, Amy Bauer, Amritha Bhat, Suzy Hunter OCHIN Inc. Research Rachel Gold,, Perry Foley EPIC Development Marla Dearing, Mary Middendorf

AIMS Center – University of Washington Background of work Part of work for a cluster randomized trial for collaborative care of perinatal depression (Trials.gov# NCT02976025) AIMS Center – University of Washington Collaborative Care Care Manager Tracking System (CMTS) OCHIN Inc. Health information network of community health centers Shared EPIC ambulatory EHR EPIC development capacity QI and clinical enhancement support The current work was carried out as part of a larger cluster randomized trial of implementing the evidence based collaborative care model for perinatal depression - national study in community health centers This study is early on in its progress and we are not going to explore the main study – instead we are talking about a feature of the standard implementation The collaborative care model is highly evidence based and is delivered by the AIMS Center of the UW Training in the delivery of this model and clinical aspects of perinatal depression care Care Management Tracking System – a free standing electronic (web based) care management system Patient registry Decision support Metrics reporting Has been in use for more than 15 years through a number of major refinements and developments with proven functionalities and many hundreds of independent sites using it for a range of implementation efforts Community health centers are part of the OCHIN Inc. health information network Shared EPIC ambulatory EHR In house functionality development – no EPIC system is the same – must be developed Quality improvement support team (operational excellence)

Implementing complex interventions Chronic illness management in primary care. Moving from volume to value Team based care Measurement based treatment Population health strategies Quality improvement processes Practice changes Operations, clinical skills, management of information A high level of evidence supports the use of a number of strategies for care of chronic illness in primary care - multidisciplinary team care (care facilitators/managers) - treatment to target (measured values for disease indicators – HbA1c, Blood pressure, depression sxs) - population strategies include patient registries to support proactive outreach and follow up - quality improvement processes with process metrics New payment models now incorporate these elements into determinations of payment - CMS initiatives including alternative payment systems (ACOs, quality metrics) Practice changes to address this move are significant - involve redesign of the primary care setting from an operations, training, and information management stand point (among others)

Health information technology supporting interventions Electronic Health Records Ubiquitous in primary care Can be used for multiple implementation strategies* Restructure: record systems, access to clinical information Quality management: quality monitoring systems, audit- feedback, reminders, Educate: decision support Evidence for support of implementation is varied - Many strategies considered distractions to users *Powell et al (2012) The electronic health record is a system that must be harnessed to support the implementation of these changes - Federal policy has facilitated their implementation (ACA and HiTECH acts) - now ubiquitous in Primary Care (Need rates of use number) - Powerful tools that are fully integrated in the flow of patient care, record keeping, and billing/coding - A range of strategies have been tested to support care processes with varying success Dashboard functionalities presenting care metrics are a common tool that balances utility with provider burden Patient registries - List of patients with common disorders and organizes key aspects of their treatment - key element for team care, measurement based treatment, and population health strategies Registries have not been built into EHRs - The complexity of EHRs and costs for modification has limited experimentation with different models of patient registries - results in a separate system that interferes with work flows and results in duplicate work for documentation

Patient registries Chronic Illness Care Functionalities Obstacles Evidence for improved outcomes Part of team based and proactive care Functionalities Ordered list of patients, Record of progress Reminders based on care pathways/protocols Facile means of communicating among team members and patients Obstacles Registries not built into EHRs Cost and complexity of modifying EHRs

Goals Build a functional care registry for perinatal depression care into an ambulatory EHR Utilize end user input to improve utility and usability Implement this tool in primary care sites providing team based care for depression (6 in first wave of trial) In the current work we wished to achieve certain key goals Build a functional care registry for perinatal depression care in an ambulatory EHR Using features that have been utilized successfully in separate care management systems Implement this tool in primary care sites providing team based care for this disorder Assess the implementation and perceived utility of the tool

Multi-disciplinary strategy - developer-user collaboration Development Process Multi-disciplinary strategy - developer-user collaboration - primary care and mental health - CMTS developers End-user input - care managers from sites using this EHR, and implemented collaborative care for perinatal depression

Evaluation Approaches Development - Cost (design, build, training elements) - Feedback and modification Implementation* - Acceptability (satisfaction) - Adoption (initiation of registry) - Appropriateness (perceived utility) - Fidelity (use of tool as designed) - Penetration (rate of eligible patients) - Sustainability (continued use) *Proctor et al (2011) The possible areas of assessment for the overall work on this registry can broadly be broken down into Development and implementation. The design and build as well as the training and support planning can be assessed in regard to overall effort - time and other kinds of cost The implementation aspects of the work

Project timeline 2/2017 ongoing 5/2016-11/2016 11/2016-2/2017 3/2017-7/2017 Development 5-11/2016 Development & Planning - 20 meetings of 1 hour - Average 9.2 participants Training Support Revision Build Hours Associated cost Production Implementation Feedback The overall timeline for the project is shown here Development of the design with workflows and functionalities = 6 months Training and support planning was at the end of this development and through the build and production period Programming and build = 2.5 months Implementation began in march of 2017 and for the current review proceeded through July 2017 Feedback and revision processes are currently underway Feedback driven enhancement plan – webinar with clinical teams

Acceptability Broad recognition of the value of the registry Quote from a site project leader: ‘Before this project, many of the staff had their own lists of patients they were concerned about, either in their head or on sticky notes. Having a list of patients to work from in Epic is useful to organize and track our perinatal patients who are at risk, and ensure that no one falls through the cracks.’ There has been feedback from the practice facilitators that the overall registry function is valuable as reflected here – however we have not completed a systematic assessment of appropriateness and satisfaction with this particular tool as there has not been sufficient time for this. We currently plan an assessment of this 10-12 months after adoption of the registry

Satisfaction Training - insufficient time for training - complex registry design - needs to be tailored to specific roles - clinical training in the intervention and the use of the registry should be integrated

Implementation Adoption First patients on registry – time from final training (3/15/17) Site Time 1 < 1 week 2 - 1 day 3 - 1 week 4 4 weeks 5 3 weeks 6 7 weeks Our measure of adoption of the tool is the time from final training regarding the registry to adding the first patients. - as you can see there is a range of outcomes from just before the final training to immediately following up to 7 weeks after this training

Implementation Penetrance – rate of eligible (screen + patients added to registry (July 2017) Site Number (% eligible) 1 21 (233%) 2 4 (36%) 3 16 (123%) 4 22 (200%) 5 47 (671%) 6 20 (167%) Because the use of the registry is dependent on having patients added we chose to use penetrance as one of our metrics of use of the tool. Shown is the data we have currently for the six sites. Since this data is pulled directly from the EHR it should be accurate. The fact that all but one site had rates greater than 100% indicated a problem with the denominator – likely this is because we set the time range for screening + too short, meaning the sites added patients to the registry who were screened before our defined period. We are currently examining this for appropriate modification

Implementation Fidelity - Adoption and Penetrance do not sufficiently reflect use of the registry - Other aspects of the registry that are more complex include frequency of review, prioritization of high risk patients for care, time to place individual patients screening+ on the registry - Insufficient time to assess these metrics We have decided that the previous measures do not sufficiently reflect appropriate use of the registry. We are currently developing possible fidelity measures of registry use that better assess this. We do not think there has been enough time to expect these clinical teams to have achieved a high level of mastery of the registry however.

Discussion Created a patient registry within an ambulatory EHR - On time and within budget - Incorporating functionality from an existing care management system - All participating sites began utilizing the system though to variable degrees - Training was insufficient for the complexity of the registry

Learnings Registry Development Implementation Participatory design was unique and useful Additional user testing before deployment by end users could have reduced some unnecessary complexity Implementation Training More time Contextualize with clinical training Incorporate end users in the development of the training Adoption – measurable but limited

Thanks from the MInD-I Team Ian M. Bennett ibennett@uw.edu https://depts.washington.edu/fammed/profile/?id=00555