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B. Holeschek



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    OS17 - Emerging Trends in mHealth: Patients' Benefits (ID 22)

    • Event: e-Health 2018 Virtual Meeting
    • Type: Oral Session
    • Track: Clinical Delivery
    • Presentations: 1
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      OS17.01 - Driving on the Right Path Towards Mobile Patient Engagement (ID 367)

      B. Holeschek, Ontario Shores Centre for Mental Health Sciences; Whitby/CA

      • Abstract
      • Slides

      Purpose/Objectives: This session will provide an overview of the lessons learned during the development and implementation of a mobile technology solution in the pursuit of innovative ways to promote, measure, and advance patient engagement. It will contribute to the body of knowledge that will help guide other organizations as they embark on this journey. Objectives: -To provide a platform for collecting patient generated data that interfaces with the EHR -To increase patient engagement and activation through interactive and patient-specific actionable interventions -To provide patients with the knowledge and skills to become activate participants and agents of change within their care through access to resources within the application

      Methodology/Approach: Building on the gains made with the implementation of the patient portal solution, the organization committed to continuing to drive patient engagement forward by partnering with a technology based company that offered a secure mobile and cloud technology platform that would interface to the EHR. With this solution, mobile remote-patient-monitoring that delivered interactive personalized interventions to individuals in support of their care plans would be enabled. The anticipated benefits of this solution were: -Accessibility-patients and clinicians not required to be connected on line to view, collect or generate data -Interactive platform-functionality includes more than a portal view of PHI. Interactive and actionable interventions that are personalized to the patient would be delivered through the mobile solution with the aim of helping patients to manage their care and have access to immediate and off-site support as needed. -Cross Platform Support (BYOD) – the solution would support a cross platform approach for devices including the support for Android, Apple and Windows devices. This encourages and supports accessibility to e-mental healthcare through the solution regardless of the device used.

      Finding/Results: The mobile patient engagement solution was implemented on 2 inpatient units and in 4 outpatient clinics. Desired outcomes around adoption and usage of the app were challenged by early issues with technological functionality including app stability, interface functionality and privacy and security concerns. Overall, these technical issues resulted in a delay in the implementation schedule, but most importantly impacted clinicians and patients perception of and their confidence in the mobile solution. Based on this experience, a number of recommendations have been developed which will guide future work in this area. -Stakeholder Engagement-early engagement of key stakeholders in the design of the interventions as well as engagement of more technically savvy patients in the education of other patient groups around the usage of the app positively impacted clinician and patient engagement. -Vendor management- clear, early communication around existing functionality required to meet scope of project -Timely communication re: planned and unplanned updates to the application is necessary to ensure good user experience -Devices- device selection should account for optimal functioning of the application and have a technical support process in place -Risk Management-clearly defined escalation process to ensure issues are addressed in a timely manner

      Conclusion/Implications/Recommendations: Patient generated personal health information is a fundamental component of patient engagement and empowerment. Selecting a mobile patient engagement solution that is integrated with the EHR, reliable, easy to use, flexible, interactive and interoperable are paramount to ensuring a positive patient experience. Equally as important is securing a solution that meets the privacy requirements necessary when dealing with personal health information. Moreover, ensuring the solution is part of the clinician’s workflow and co-designing patient- specific interventions with clinicians, as well as integrating outcomes-based research is essential to a the ultimate success of a new mobile technology strategy.

      140 Character Summary: A mobile patient engagement solution was implemented and desired outcomes around adoption and usage of the app were challenged with many lessons learned.

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    OS30 - Enabling High Value Analytics (ID 44)

    • Event: e-Health 2018 Virtual Meeting
    • Type: Oral Session
    • Track: Clinical Delivery
    • Presentations: 1
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      OS30.02 - Using Advanced Analytics to Predict Clinical Aggression in Mental Health (ID 368)

      B. Holeschek, Ontario Shores Centre for Mental Health Sciences; Whitby/CA

      • Abstract
      • Slides

      Purpose/Objectives: Leveraging the advanced analytics capabilities inherent in a fully integrated EHR, the hospital is piloting an analytics solution with the goal of improving capabilities to predict and clearly identify clinical aggression (CA) and enhance staff capacity to respond effectively to risks of future CA from a diverse patient cohort. Objectives: -Reducing incidents of CA, harm to others, and restraint/seclusion use while increasing therapeutic engagement -Increase confidence and competence of staff in identifying and managing the antecedents of CA -Improve safety for both clinicians and patients

      Methodology/Approach: A number of risk assessment tools for imminent aggression and discrete data are captured in the EHR; however, they have limited predictive power with respect to indicating when a patient could become aggressive. Additionally, the predictive utility of these scales are in the very near term; and the ideal predictive model would be able to predict CA risk days prior to its occurrence to have time to implement robust interventions. To address this, we have partnered with an expert to create a neural network predictive model that will leverage data found within unstructured and structured data sources in the EHR. If the model is successful at predicting CA, the implications for next steps are vast, including ability of clinicians to be alerted to become proactive to improve patient outcomes. Validation of the predictive model was completed over 2 months with a pilot focusing on the predictive models ability to accurately predict CA incidents with current data over a four month period. Pre-implementation data will be utilized to compare prediction performance rates with implementation groups. A comparison of expected verses actual number of CA incidents will be conducted utilizing reports which identify the total number of CA incidents against the expected number of CA incidents. Control charts reviewing trends for the CA incidents, harm to others, and restraint & seclusion use (incidents and duration) will be utilized to evaluate. A chart audit of CA occurances will be conducted to identify what the care planning intervention.

      Finding/Results: Implementation of the predictive model is still in progress. An evaluation framework has been developed and the predictive model will be evaluated along the following domains: -Model Performance- How accurate is the model in predicting CA? -Outcomes-Are there any improvements in key outcome and balancing indicators: Incidents of CA, harm to others (incident data – harm to staff or patients), and restraint & seclusion use All findings from the pilot will be presented at the 2018 eHealth conference.

      Conclusion/Implications/Recommendations: The implications of creating a predictive tool for CA are vast. This approach will allow staff to identify dynamic risk factors that contribute to the risk for CA and subsequently facilitate proactive refinement of care plans to mitigate or address the risk and potentially reducing aggressive incidents and restraint and seclusion use. Moreover, from a risk management and operational perspective, implementation of a predictive alerting tool for CA has the potential to positively impact patient incidents as well as staff injury rates related to patient aggression, and the subsequent sick time and costs to the healthcare system associated with these events.

      140 Character Summary: This predictive solution has the potential to reduce clinical aggression and restraint/seclusion use which would improve patient outcomes and staff safety.

      Only Active Members that have purchased this event or have registered via an access code will be able to view this content. To view this presentation, please login or select "Add to Cart" and proceed to checkout.