Project HealthDesign: A preliminary program-level report Gail R. Casper, RN, PhD1, Patricia Flatley Brennan, RN, PhD1 1 University of Wisconsin, Madison, WI Abstract Advancements in the health information technology that brought personal health records to individuals have opened the door to new insights concerning the cues people use to monitor health in their everyday lives. In order to evaluate the impact of capturing, storing, and integrating these observations of daily living (ODLs) into the clinical care process, Project HealthDesign selected five teams to create and analyze mobile health applications with ODLs in mind. These teams targeted underserved, minority populations with at least two chronic conditions. Using thirdparty platforms for storage, the teams were expected to bring the ODLs into the clinical workflow through the EHR. ODLs were successfully captured, interpreted and displayed; however technical and policy barriers challenge their integration outside of the mobile application. This paper serves as a preliminary, program-level report distinct from the publication of evaluation results from individual teams. Introduction The changing health care environment is accompanied by a growing recognition of the importance of patient engagement in everyday health through inclusion of patient perspective in health, health care and in clinical decision making. Active participation of patients in their own care and in the health care process is key to achieving the vision of the current health reform initiatives. Patient engagement provides the foundation for active participation of patients, and engenders a new vision of health services, one grounded in a two-way exchange of information between patients and clinicians. Patient engagement involves a broad set of beliefs, attitudes, and actions undertaken by clinicians and patients, in which the patient enters into a partnership with the clinician. In this partnership information, preferences, knowledge and responsibility are shared to achieve the outcomes of their health efforts. Full engagement by a patient requires comprehending one’s own health state, interpreting it, and knowing what actions to take and when to take them1. Sharing these individual observations and insights with clinicians brings the everyday life of the person into the clinical encounter, and constructs a more robust understanding of the patient and their health challenges. Project HealthDesign (PHD), a national program of the Robert Wood Johnson Foundation, sought to accelerate progress towards patient engagement through building better information tools to support patients’ awareness and their ability to take action in everyday living. In this paper we describe the information technologies and novel data types that expand the idea of patient engagement from a one-way process of integrating patients into clinical care to a two-way process of using data and technology to enable clinicians to better understand a patient’s every day health concerns and practices and to equip patients with the tools to help them identify, capture, interpret and act on health observations and health practices in everyday life. We introduce the idea of observations of daily living (ODLs) as a valid data type with the potential 1) to foster patient engagement as a bi-directional process, 2) to illustrate how commercial technologies, properly deployed, can better engage patients in their own health on a-day-to-day basis, and 3) bring the everyday experience of patients into the health care encounter. Enriching the concept of patient engagement through better understanding of patients’ health in everyday living stands to enhance the efficiency of the clinical encounter and the effectiveness of health services, and accelerate progress towards the benefits promised by contemporary health reform initiatives. Background: Project HealthDesign At the inception of Project HealthDesign (www.projecthealthdesign.org), personal health records (PHRs) served primarily as windows into the electronic health record (EHR), provided by hospitals and clinics as a way to give patients a more complete understanding of their health and health care. Other PHRs had been developed as freestanding web-based repositories of patient-entered information or as product offerings from pharmacies or health plans, providing users with a glimpse of the data collected about them.
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Project HealthDesign: Rethinking the Power and Potential of Personal Health Records is a $10-million national program funded through the Robert Wood Johnson Foundation’s (RWJF) Pioneer Portfolio. It conceptualized PHRs as systems of interlocking components that had the capacity to capture, store and make accessible information relevant to an individual’s health. These envisioned interoperable PHR systems could include information from a variety of sources, including a patient’s clinical records as well as his or her own observations about day-to-day health experiences and feelings. PHRs needed to function not just to capture and store data but also include specialized tools to help people visualize, interpret and manage this information. A new architecture was required, one in which various PHR applications can draw data from different sources for decision support or care coordination. The idea of what constitutes the purpose and function of a PHR continues to evolve. PHR platform services (third-party platforms), such as Microsoft HealthVault and The Carrot.com, provide places to store health data independent of where or how they are collected. Home-based sensors and everyday devices such as smart phones provide on-the-spot monitors of important dimensions of health in everyday living The first round of funding of Project HealthDesign (PHD) aimed to stimulate innovations in the development of personal health record systems by transforming the concept of PHRs as data collection tools to PHRs as platforms for action. This round of funding was grounded in three key ideas: 1) design PHRs to address the personal health information challenges faced by people in their everyday lives; 2) deliberately separate the data from the applications; and 3) collaboratively develop a common infrastructure and employ shared design approaches. Nine teams participated and created a broad range of tools that addressed specific and complex self-management tasks2. A key insight gleaned during this initial round of Project HealthDesign was the importance of the subtle but systematic cues that people attend to as they monitor their health state. People often relied on information taken from these Observations of Daily Living (ODLs) to gauge how they were progressing, guide them in their choices of health actions and determine if the actions they have taken were producing the desired effect. Examples of ODLs ran the gamut from the moods teens experienced in their everyday lives, to fluctuations in work- or home-related stress or to exercise/eating patterns exhibited by a person with observations. These observations could also be useful to clinicians, for whom a richer picture of a patient’s experience could yield insights that lead to better treatment regimens and better outcomes. Both patients and clinicians benefit from PHRs that enable clinicians to keep in touch with their patients between office visits. Examples of the potential benefits of capturing and interpreting ODLs include: monitoring sleep-rest patterns and exploring the relationship to depression; noting how daily food choices contribute to fluctuations in blood pressure; or examining the impact of family stress on eating patterns of persons with diabetes. In the second round of funding, Project HealthDesign’s primary aim was to test whether and how information about patterns of everyday living (ODLs) could be collected and interpreted such that patients could take action and clinicians could integrate new insights into clinical care processes. Building upon PHD phase 1 and with it, the emergence of ODLs, the second phase of Project HealthDesign supported multidisciplinary teams to work with a target patient population to demonstrate the impact of the capture, storage and integration of ODLs into both self-management and clinical care processes. Specifically, each selected team worked with patients with chronic illness and their health care providers to identify ODLs that were meaningful to each. They were charged with analyzing, interpreting and creating separate visualizations of the ODL data in ways that were meaningful to each group. In a 6-month or longer time period, grantees conducted evaluation studies to appraise the impact of ODLs on patient outcomes as well as clinical workflows. Key expectations of the grantee teams included:
Recruit patients who are minority, underserved and/or not typically included in such technology research AND recruit their health care providers Identify ODLs of patients with at least two chronic conditions who would experience a clinical encounter Use third party platform for storage and sharing of ODL data Integrate ODL data into clinical workflows through the electronic health record (EHR).
Brief descriptions of the funded projects follow: BreathEasy – RTI International and Virginia Commonwealth University Led by Barbara Massoudi, PhD and Steve Rothemich, MD, The BreathEasy team designed a mobile application built on the latest clinical guidelines for treatment and self-monitoring for patients with asthma. Patients used the application on smartphones to capture and record ODLs including the use of asthma control and rescue medications,
193
symptom levels, quality of life and smoking. Using a web-based dashboard with simple analysis and visualization tools, clinic nurses viewed the patients’ data weekly, evaluated their health status, and continued or modified treatment according to clinic protocols. Chronology.MD – University of California-Berkeley, Healthy Communities Foundation, and University of California-San Francisco The Chronology.MD team led by Deryk Van Brunt, PhD and Linda Neuhauser, PhD created two mobile applications to help young adults with Crohn’s disease to track and create visually aided narratives of their health state and responses to treatments. Using iPads and other mobile devices, the project enabled patients to self-track ODLs such as food tolerance, pain and energy levels alongside clinical symptoms or measurements such as Vitamin B12 levels. Chronology.MD allowed patients to more accurately communicate with their clinicians about their health status. dwellSense – Carnegie Mellon University The dwellSense team, led by Anind Dey, PhD and Matthew Lee, PhD developed new sensor technologies to monitor specified behaviors of elders who were at risk for cognitive decline. In-home sensors monitored routine tasks, including taking medications, making and receiving phone calls and preparing coffee, and provided trustworthy data for longitudinal assessment of cognitive and functional performance. This sensor data could be used by the participants, caregivers, or clinicians, to evaluate performance, detect changes and modify behavior as warranted. Early detection of cognitive decline provided the opportunity to monitor deterioration and prevent an unsafe living situation requiring transition to long-term care. Estrellita – University of California, Irvine Gillian Hayes, PhD’s Estrellita team created a mobile application to collect and record data from newly discharged high-risk infants and their primary caregiver. Caregivers used the Estrellita application to record ODLs like the baby’s fussiness, diapering and weight as well as caregiver’s stress and risk for post-partum depression. This application allowed the caregivers to more easily interface with the Early Development Assessment Center and specialists to improve care and communication. The app also displayed clinical appointments and encouraged the caregiver to review the ODL data and ask questions during appointments. iN Touch – San Francisco State University Katherine Kim, PhDc’s iN Touch team examined how collecting ODLs using a mobile application on an iPod Touch impacted low-income teens and young adults who were managing obesity and stress. The project utilized applications and other emerging technologies that are popular among young people to make monitoring ODLs such as physical activity, food intake, socialization and mood more convenient and meaningful. In addition, the technology allowed participants to easily share the data with their lay health coaches and clinical care teams in order to help set health goals, track their progress and improve their health. Methods Grantee Selection Project HealthDesign solicited participants through a competitive grant process. Critical selection criteria included a clear focus on a target population; the potential for positively impacting on health; willingness to participate in a collaborative design process; the strength of the interdisciplinary design team and strong plan for patient and clinician engagement. From approximately 170 applicants, five teams were selected to receive funding for two years. Collaborative Design Process We employed a structured design process to stimulate individual and collaborative project development. range of PHR applications (PHAs). Teams met for intensive, two-day collaborative workshops five times over the two-year period. The general design process had three phases: refine/design phase – teams revised and refined their proposals after meeting as a group to share proposals, identify common approaches and strategies for capturing and interpreting ODLs; implementation phase – teams worked with their target population to validate ODLs and the strategies used to capture and interpret them. They finalized plans for sharing data with their clinical partners and approaches to integrating ODLs into the clinical workflow; and
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evaluation phase – the impact of capturing, displaying and sharing ODLs on patient outcomes and clinical workflow was appraised.
Devices used to Collect and Share ODLs The PHD projects provided the mobile devices required by patient participants for collecting, viewing and sharing ODLs. Devices employed by the teams included smartphones, iPhones, iPod Touches and iPads. Most participants owned cellphones, but teams quickly discovered incompatibilities between the operating systems and the ODL applications. Sharing phones among family members and friends also prohibited dedicated use for the purpose of data collection and sharing. Regulatory and Assurance Consultancy In recognition of the rapid advances occurring in the technology space and related policy arena, we believed it would be crucial to have rapid information available to teams as they developed and implemented their applications. We engaged a regulatory and assurance consultant to advise the individual grantee teams and the national program office (NPO) on certification requirements, emerging legislation, applicable regulations and case law precedent that may alter the consequences of data-sharing between patients and clinicians. The dual-focus of Project HealthDesign, on interpretation and integration of patient-generated data, raised important needs for ensuring compliance with state and federal guidelines regarding data transfer and storage. This consultant created a summary of the state-level and national regulations and laws to which our grantees adhered. In addition, blogs written by Robert Belfort, JD and Deven McGraw, JD addressing many of the policy issues faced or anticipated during the conduct of the projects can be found in the Project HealthDesign blog. Technical Consultancy: Grantee teams had access to the services of Sujansky and Associates for technical consultation. Their work was particularly important for designing and providing documentation of the technical architecture, and for advice on implementation of a third party platform. In addition, Sujansky and Associates provided periodic reports on the status of the industry and the uptake of the products of Project HealthDesign, also available on the program’s website. Results Because this is a program-level report, we present here both observations and interpretation of them in this space. Recruitment of Minority/Underserved Patient Participants A total of 160 patients (69%) of the 233 who were invited to participate were enrolled in the projects. The response rate ranged from 42% to 94%. The number of patient participants varied by project with one recruiting as few as 14 and another as many as 49. Patient participants in Project HealthDesign represent ages across the lifespan. Project teams targeted minority and underserved participants and were successful in recruiting and retaining them. Table 1 below summarizes patient participant demographics. The number of respondents varies across variables due to differences in questions or categorization by individual teams, e.g. some teams did not ask about race or ethnicity; others used different education categories. In general, PHD teams were successful both in recruitment and retention of minority and underserved participants, but it is likely that providing the mobile device influenced these rates3.
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Table 1. Patient participant demographics across teams Age N=137
≤ 20 years 21-54 years ≥55 years
29% 61% 10%
Race N=98
Black / African American White Other Non-Hispanic or Latino Hispanic High School or less Some college/post-secondary Completed college ≤4 months 5 months ≥ 6 months
52% 35% 13% 69% 31% 64% 21% 15% 11% 9% 80 %
Ethnicity N=99 Education N=119 Time in intervention N=130
Provider Recruitment Fifty providers were invited to participate and 41 consented (82%); acceptance rates ranged from 47% to 100%. Projects invited as few as four physician providers and as many as 21. Provider refusals were more likely to occur in the practices that did not have a direct role in the project. Two different workflow processes for clinical integration of ODL data were utilized: a triage model and a direct model. In the triage model ODL data were reviewed by nurses, a lay health coach or a caregiver; if an emergent issue was identified the information was passed to the appropriate clinician. Most of the projects employed an intermediary staff person to triage the data. In the direct model, patients brought collected ODL data to routinely scheduled appointments. Observations of Daily Living (ODLs) The work of the PHD grantees revealed that, in addition to the well-recognized signs, symptoms and clinical findings, a complementary set of observations and activities that are centered in the patient’s worldview exist. We discerned that patients identify and attend to subtle cues, we termed Observations of Daily Living (ODLs). These cues serve as indicators of the need to take health actions (e.g. contact a clinician, alter a meal plan). These ODL data are defined uniquely, sometimes by the patient alone and sometimes in consultation with a clinician; they complement the more familiar signs and symptoms and fostering engagement by providing a patient-centered perspective on health in everyday living. A total of 36 ODLs were identified for tracking by patient and clinician participants across the five teams. The number of ODLs per team ranged from four to ten. Because of the unique and idiosyncratic nature of ODLs, there was little overlap in ODLs across teams. After gaining experience with tracking ODLs during the evaluation phase of the project, several additional ODLs were identified by participants in several of the teams. ODLs are displayed in Table 2 below. The collection and tracking of ODLs was not described as burdensome for patients. However, a commonly noted patient workflow challenge was being too "ill", "overwhelmed", or "busy" for data collection. Life events— hospitalizations, moving, etc.—also sometimes halted data collection at a time when it may have been most important. Two projects reported that theft or loss of device challenge patient’s ODL collection in two instances. Additional challenges faced included: maintaining contact with the participants was difficult when they didn’t use email, participants sharing cell phones with others, lack of an iTunes account, and insufficient or lack of a phone plan. Some participants reported that they rarely looked at their data, partly because they didn't have access to computers to use for viewing the reports. Only one project team reported facing a challenge with patients who were unfamiliar with technology or having a difficult time learning how to use the application.
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Table 2. ODLs collected by patient participants Peak flow rates Weight Medication taking Controller med use Abdominal pain Make phone call Use of rescue meds Activity: miles/day Receive phone call Reason for rescue Medications Make coffee med use Test results Asthma trigger Energy level Type of physical Stress level activity Sleep Activity Limitations Journal Subjective level of Trigger foods symptoms Sleep pattern Smoking
Weight Parent /Caregiver mood Parent/Caregiver stress Post-partum depression Fussiness Diaper changes – frequency Bonding activities Appointments
Food entry Mood Socializing Physical activity
Third Party Platform Use Two of the five teams used a third party platform; those used were TheCarrot.com and Amazon Cloud. The remaining teams reported that it was more time-efficient to use a local server for storage and sharing of data with clinical entities. Sample technical architectures are depicted in Figures 1and 2 below. Figure 1. Did not use a third party platform
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Figure 2. Used a third party platform (The Carrot)
Although a goal of Project HealthDesign was to incorporate the use of a third-party platform for data sharing and storage, this did not occur in three of five cases. Third party platforms that were used were TheCarrot.com and Amazon Cloud. Reasons reported for not using a third party platform included: the third party platform required more customization for individual ODLs than originally anticipated, thus programming for the platform proved to be burdensome and not an effective use of investigator’s time. Some reported that it was easier to build their own tools. In addition, some teams stated that their participants would not be able to access the data when the project was completed, which negated the benefit of using the third party platform. Integration of ODL data into electronic health record (EHR) Due to constraints on the clinical side, direct entry of ODLs into a dedicated space in the EHR did not occur. Examples of such constraints included: institutions were in the process of implementing a new system or were limited by capacity of a legacy system; insufficient IT staff to enable integration; or concerns about clinician and institutional liability for ODL data. Two projects were able to insert a link to the ODL data from the EHR. Discussion Engaging patients and their clinicians more robustly in everyday health is key to achieving positive health outcomes. Continuing advances in health information technology provide innovative tools to support this engagement. Policies are shifting and must continue to shift to support patient-clinician engagement. An example of this shift are the DHHS Meaningful Use Stage 3 proposed core and/or menu recommendations supporting patient engagement – including the ability to upload or report patient generated data such as ODLs or health goals into a chosen primary place, for example a shared EHR, Portal, or PHR4. The Project HealthDesign program demonstrated that emerging computer and telecommunications technologies support patient engagement in two ways: 1) by providing easy to use and interesting tools for the collection, display and sharing of health-relevant data, and 2) by serving as a mechanism to present guidance for health action at the point of everyday living. Enriching the concept of patient engagement through better understanding of patients’ health in everyday living stands to enhance the efficiency of the clinical encounter and the effectiveness of health services, and accelerate progress towards the benefits promised by contemporary health reform initiatives. We learned that patients will use information technologies that are built to fit into their everyday lives and help them to capture, interpret and share health-related signals that are important to them with their health care professionals. We also learned a lot about integrating patient generated data into clinical care. We observed five very different approaches to engaging patients and clinicians in the review and interpretation of ODLs. These grantee teams
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illustrated how between-visit services could be structured to keep the patient engaged and to make the best use of the data. Raising awareness of patient-defined, patient-generated data such as ODLs and is a necessary starting point for fostering patient engagement through healthy action. Necessary too are resolving uncertainties regarding the application of health privacy and security laws, and on the ability for clinicians to act on patient generated data. The technological infrastructure necessary for capturing, interpreting, displaying and storing patient generated data must be developed and made available during the health encounter and in the patients’ everyday life. Key to adoption of such models of health care based on bi-directional exchange of relevant information is a legal and policy climate that encourages (or at least does not discourage) patients and health care providers from implementing clinical workflow models similar to those explored by Project HealthDesign. In many cases, customary laws protecting health data do not apply to technology used by patients to collect, store and share health data. In cases where legal guidance exists, they often do not provide clear guidance on how to comply in circumstances where data is being shared routinely by patients. Significant technology, policy and practice workflow challenges remain; strategies to address and resolve these will be illuminated in the reports of the individual Project HealthDesign teams.
Acknowledgement Support for this research was provided by the Robert Wood Johnson Foundation through a grant from its Pioneer Portfolio. The authors acknowledge the contributions of the five Grantee teams and the Project HealthDesign National Program Office staff. We also thank Kathy Johnson, RN, PhDc and Madhusudan Rajendran for their work in synthesizing teams’ common data elements References 1. 2. 3. 4.
NAQC (2010). Guiding Principles for Patient Engagement http://www.gwumc.edu/healthsci/departments/nursing/naqc/documents/Patient_Engagement_Guiding.pdf Last accessed March 14, 2013. Project HealthDesign Supplement, J Biomedical Informatics, 2010;43(5): Supplement 1, S1-S56 PF Brennan, Valdez R. Medical Informatics. In MC Gibbons (Ed). eHealth Solutions for Healthcare Disparities, 2008, 93-108. Springer. DHHS (2012). HITPC Stage 3 Request for Comment http://www.healthit.gov/sites/default/files/draft_stage3_rfc_07_nov_12.pdf Last accessed March 13, 2013.
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Project HealthDesign: Rethinking the Power and Potential of Personal Health Records is a $10-million national program funded through the Robert Wood Johnson Foundation’s (RWJF) Pioneer Portfolio. It conceptualized PHRs as systems of interlocking components that had the capacity to capture, store and make accessible information relevant to an individual’s health. These envisioned interoperable PHR systems could include information from a variety of sources, including a patient’s clinical records as well as his or her own observations about day-to-day health experiences and feelings. PHRs needed to function not just to capture and store data but also include specialized tools to help people visualize, interpret and manage this information. A new architecture was required, one in which various PHR applications can draw data from different sources for decision support or care coordination. The idea of what constitutes the purpose and function of a PHR continues to evolve. PHR platform services (third-party platforms), such as Microsoft HealthVault and The Carrot.com, provide places to store health data independent of where or how they are collected. Home-based sensors and everyday devices such as smart phones provide on-the-spot monitors of important dimensions of health in everyday living The first round of funding of Project HealthDesign (PHD) aimed to stimulate innovations in the development of personal health record systems by transforming the concept of PHRs as data collection tools to PHRs as platforms for action. This round of funding was grounded in three key ideas: 1) design PHRs to address the personal health information challenges faced by people in their everyday lives; 2) deliberately separate the data from the applications; and 3) collaboratively develop a common infrastructure and employ shared design approaches. Nine teams participated and created a broad range of tools that addressed specific and complex self-management tasks2. A key insight gleaned during this initial round of Project HealthDesign was the importance of the subtle but systematic cues that people attend to as they monitor their health state. People often relied on information taken from these Observations of Daily Living (ODLs) to gauge how they were progressing, guide them in their choices of health actions and determine if the actions they have taken were producing the desired effect. Examples of ODLs ran the gamut from the moods teens experienced in their everyday lives, to fluctuations in work- or home-related stress or to exercise/eating patterns exhibited by a person with observations. These observations could also be useful to clinicians, for whom a richer picture of a patient’s experience could yield insights that lead to better treatment regimens and better outcomes. Both patients and clinicians benefit from PHRs that enable clinicians to keep in touch with their patients between office visits. Examples of the potential benefits of capturing and interpreting ODLs include: monitoring sleep-rest patterns and exploring the relationship to depression; noting how daily food choices contribute to fluctuations in blood pressure; or examining the impact of family stress on eating patterns of persons with diabetes. In the second round of funding, Project HealthDesign’s primary aim was to test whether and how information about patterns of everyday living (ODLs) could be collected and interpreted such that patients could take action and clinicians could integrate new insights into clinical care processes. Building upon PHD phase 1 and with it, the emergence of ODLs, the second phase of Project HealthDesign supported multidisciplinary teams to work with a target patient population to demonstrate the impact of the capture, storage and integration of ODLs into both self-management and clinical care processes. Specifically, each selected team worked with patients with chronic illness and their health care providers to identify ODLs that were meaningful to each. They were charged with analyzing, interpreting and creating separate visualizations of the ODL data in ways that were meaningful to each group. In a 6-month or longer time period, grantees conducted evaluation studies to appraise the impact of ODLs on patient outcomes as well as clinical workflows. Key expectations of the grantee teams included:
Recruit patients who are minority, underserved and/or not typically included in such technology research AND recruit their health care providers Identify ODLs of patients with at least two chronic conditions who would experience a clinical encounter Use third party platform for storage and sharing of ODL data Integrate ODL data into clinical workflows through the electronic health record (EHR).
Brief descriptions of the funded projects follow: BreathEasy – RTI International and Virginia Commonwealth University Led by Barbara Massoudi, PhD and Steve Rothemich, MD, The BreathEasy team designed a mobile application built on the latest clinical guidelines for treatment and self-monitoring for patients with asthma. Patients used the application on smartphones to capture and record ODLs including the use of asthma control and rescue medications,
193
symptom levels, quality of life and smoking. Using a web-based dashboard with simple analysis and visualization tools, clinic nurses viewed the patients’ data weekly, evaluated their health status, and continued or modified treatment according to clinic protocols. Chronology.MD – University of California-Berkeley, Healthy Communities Foundation, and University of California-San Francisco The Chronology.MD team led by Deryk Van Brunt, PhD and Linda Neuhauser, PhD created two mobile applications to help young adults with Crohn’s disease to track and create visually aided narratives of their health state and responses to treatments. Using iPads and other mobile devices, the project enabled patients to self-track ODLs such as food tolerance, pain and energy levels alongside clinical symptoms or measurements such as Vitamin B12 levels. Chronology.MD allowed patients to more accurately communicate with their clinicians about their health status. dwellSense – Carnegie Mellon University The dwellSense team, led by Anind Dey, PhD and Matthew Lee, PhD developed new sensor technologies to monitor specified behaviors of elders who were at risk for cognitive decline. In-home sensors monitored routine tasks, including taking medications, making and receiving phone calls and preparing coffee, and provided trustworthy data for longitudinal assessment of cognitive and functional performance. This sensor data could be used by the participants, caregivers, or clinicians, to evaluate performance, detect changes and modify behavior as warranted. Early detection of cognitive decline provided the opportunity to monitor deterioration and prevent an unsafe living situation requiring transition to long-term care. Estrellita – University of California, Irvine Gillian Hayes, PhD’s Estrellita team created a mobile application to collect and record data from newly discharged high-risk infants and their primary caregiver. Caregivers used the Estrellita application to record ODLs like the baby’s fussiness, diapering and weight as well as caregiver’s stress and risk for post-partum depression. This application allowed the caregivers to more easily interface with the Early Development Assessment Center and specialists to improve care and communication. The app also displayed clinical appointments and encouraged the caregiver to review the ODL data and ask questions during appointments. iN Touch – San Francisco State University Katherine Kim, PhDc’s iN Touch team examined how collecting ODLs using a mobile application on an iPod Touch impacted low-income teens and young adults who were managing obesity and stress. The project utilized applications and other emerging technologies that are popular among young people to make monitoring ODLs such as physical activity, food intake, socialization and mood more convenient and meaningful. In addition, the technology allowed participants to easily share the data with their lay health coaches and clinical care teams in order to help set health goals, track their progress and improve their health. Methods Grantee Selection Project HealthDesign solicited participants through a competitive grant process. Critical selection criteria included a clear focus on a target population; the potential for positively impacting on health; willingness to participate in a collaborative design process; the strength of the interdisciplinary design team and strong plan for patient and clinician engagement. From approximately 170 applicants, five teams were selected to receive funding for two years. Collaborative Design Process We employed a structured design process to stimulate individual and collaborative project development. range of PHR applications (PHAs). Teams met for intensive, two-day collaborative workshops five times over the two-year period. The general design process had three phases: refine/design phase – teams revised and refined their proposals after meeting as a group to share proposals, identify common approaches and strategies for capturing and interpreting ODLs; implementation phase – teams worked with their target population to validate ODLs and the strategies used to capture and interpret them. They finalized plans for sharing data with their clinical partners and approaches to integrating ODLs into the clinical workflow; and
194
evaluation phase – the impact of capturing, displaying and sharing ODLs on patient outcomes and clinical workflow was appraised.
Devices used to Collect and Share ODLs The PHD projects provided the mobile devices required by patient participants for collecting, viewing and sharing ODLs. Devices employed by the teams included smartphones, iPhones, iPod Touches and iPads. Most participants owned cellphones, but teams quickly discovered incompatibilities between the operating systems and the ODL applications. Sharing phones among family members and friends also prohibited dedicated use for the purpose of data collection and sharing. Regulatory and Assurance Consultancy In recognition of the rapid advances occurring in the technology space and related policy arena, we believed it would be crucial to have rapid information available to teams as they developed and implemented their applications. We engaged a regulatory and assurance consultant to advise the individual grantee teams and the national program office (NPO) on certification requirements, emerging legislation, applicable regulations and case law precedent that may alter the consequences of data-sharing between patients and clinicians. The dual-focus of Project HealthDesign, on interpretation and integration of patient-generated data, raised important needs for ensuring compliance with state and federal guidelines regarding data transfer and storage. This consultant created a summary of the state-level and national regulations and laws to which our grantees adhered. In addition, blogs written by Robert Belfort, JD and Deven McGraw, JD addressing many of the policy issues faced or anticipated during the conduct of the projects can be found in the Project HealthDesign blog. Technical Consultancy: Grantee teams had access to the services of Sujansky and Associates for technical consultation. Their work was particularly important for designing and providing documentation of the technical architecture, and for advice on implementation of a third party platform. In addition, Sujansky and Associates provided periodic reports on the status of the industry and the uptake of the products of Project HealthDesign, also available on the program’s website. Results Because this is a program-level report, we present here both observations and interpretation of them in this space. Recruitment of Minority/Underserved Patient Participants A total of 160 patients (69%) of the 233 who were invited to participate were enrolled in the projects. The response rate ranged from 42% to 94%. The number of patient participants varied by project with one recruiting as few as 14 and another as many as 49. Patient participants in Project HealthDesign represent ages across the lifespan. Project teams targeted minority and underserved participants and were successful in recruiting and retaining them. Table 1 below summarizes patient participant demographics. The number of respondents varies across variables due to differences in questions or categorization by individual teams, e.g. some teams did not ask about race or ethnicity; others used different education categories. In general, PHD teams were successful both in recruitment and retention of minority and underserved participants, but it is likely that providing the mobile device influenced these rates3.
195
Table 1. Patient participant demographics across teams Age N=137
≤ 20 years 21-54 years ≥55 years
29% 61% 10%
Race N=98
Black / African American White Other Non-Hispanic or Latino Hispanic High School or less Some college/post-secondary Completed college ≤4 months 5 months ≥ 6 months
52% 35% 13% 69% 31% 64% 21% 15% 11% 9% 80 %
Ethnicity N=99 Education N=119 Time in intervention N=130
Provider Recruitment Fifty providers were invited to participate and 41 consented (82%); acceptance rates ranged from 47% to 100%. Projects invited as few as four physician providers and as many as 21. Provider refusals were more likely to occur in the practices that did not have a direct role in the project. Two different workflow processes for clinical integration of ODL data were utilized: a triage model and a direct model. In the triage model ODL data were reviewed by nurses, a lay health coach or a caregiver; if an emergent issue was identified the information was passed to the appropriate clinician. Most of the projects employed an intermediary staff person to triage the data. In the direct model, patients brought collected ODL data to routinely scheduled appointments. Observations of Daily Living (ODLs) The work of the PHD grantees revealed that, in addition to the well-recognized signs, symptoms and clinical findings, a complementary set of observations and activities that are centered in the patient’s worldview exist. We discerned that patients identify and attend to subtle cues, we termed Observations of Daily Living (ODLs). These cues serve as indicators of the need to take health actions (e.g. contact a clinician, alter a meal plan). These ODL data are defined uniquely, sometimes by the patient alone and sometimes in consultation with a clinician; they complement the more familiar signs and symptoms and fostering engagement by providing a patient-centered perspective on health in everyday living. A total of 36 ODLs were identified for tracking by patient and clinician participants across the five teams. The number of ODLs per team ranged from four to ten. Because of the unique and idiosyncratic nature of ODLs, there was little overlap in ODLs across teams. After gaining experience with tracking ODLs during the evaluation phase of the project, several additional ODLs were identified by participants in several of the teams. ODLs are displayed in Table 2 below. The collection and tracking of ODLs was not described as burdensome for patients. However, a commonly noted patient workflow challenge was being too "ill", "overwhelmed", or "busy" for data collection. Life events— hospitalizations, moving, etc.—also sometimes halted data collection at a time when it may have been most important. Two projects reported that theft or loss of device challenge patient’s ODL collection in two instances. Additional challenges faced included: maintaining contact with the participants was difficult when they didn’t use email, participants sharing cell phones with others, lack of an iTunes account, and insufficient or lack of a phone plan. Some participants reported that they rarely looked at their data, partly because they didn't have access to computers to use for viewing the reports. Only one project team reported facing a challenge with patients who were unfamiliar with technology or having a difficult time learning how to use the application.
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Table 2. ODLs collected by patient participants Peak flow rates Weight Medication taking Controller med use Abdominal pain Make phone call Use of rescue meds Activity: miles/day Receive phone call Reason for rescue Medications Make coffee med use Test results Asthma trigger Energy level Type of physical Stress level activity Sleep Activity Limitations Journal Subjective level of Trigger foods symptoms Sleep pattern Smoking
Weight Parent /Caregiver mood Parent/Caregiver stress Post-partum depression Fussiness Diaper changes – frequency Bonding activities Appointments
Food entry Mood Socializing Physical activity
Third Party Platform Use Two of the five teams used a third party platform; those used were TheCarrot.com and Amazon Cloud. The remaining teams reported that it was more time-efficient to use a local server for storage and sharing of data with clinical entities. Sample technical architectures are depicted in Figures 1and 2 below. Figure 1. Did not use a third party platform
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Figure 2. Used a third party platform (The Carrot)
Although a goal of Project HealthDesign was to incorporate the use of a third-party platform for data sharing and storage, this did not occur in three of five cases. Third party platforms that were used were TheCarrot.com and Amazon Cloud. Reasons reported for not using a third party platform included: the third party platform required more customization for individual ODLs than originally anticipated, thus programming for the platform proved to be burdensome and not an effective use of investigator’s time. Some reported that it was easier to build their own tools. In addition, some teams stated that their participants would not be able to access the data when the project was completed, which negated the benefit of using the third party platform. Integration of ODL data into electronic health record (EHR) Due to constraints on the clinical side, direct entry of ODLs into a dedicated space in the EHR did not occur. Examples of such constraints included: institutions were in the process of implementing a new system or were limited by capacity of a legacy system; insufficient IT staff to enable integration; or concerns about clinician and institutional liability for ODL data. Two projects were able to insert a link to the ODL data from the EHR. Discussion Engaging patients and their clinicians more robustly in everyday health is key to achieving positive health outcomes. Continuing advances in health information technology provide innovative tools to support this engagement. Policies are shifting and must continue to shift to support patient-clinician engagement. An example of this shift are the DHHS Meaningful Use Stage 3 proposed core and/or menu recommendations supporting patient engagement – including the ability to upload or report patient generated data such as ODLs or health goals into a chosen primary place, for example a shared EHR, Portal, or PHR4. The Project HealthDesign program demonstrated that emerging computer and telecommunications technologies support patient engagement in two ways: 1) by providing easy to use and interesting tools for the collection, display and sharing of health-relevant data, and 2) by serving as a mechanism to present guidance for health action at the point of everyday living. Enriching the concept of patient engagement through better understanding of patients’ health in everyday living stands to enhance the efficiency of the clinical encounter and the effectiveness of health services, and accelerate progress towards the benefits promised by contemporary health reform initiatives. We learned that patients will use information technologies that are built to fit into their everyday lives and help them to capture, interpret and share health-related signals that are important to them with their health care professionals. We also learned a lot about integrating patient generated data into clinical care. We observed five very different approaches to engaging patients and clinicians in the review and interpretation of ODLs. These grantee teams
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illustrated how between-visit services could be structured to keep the patient engaged and to make the best use of the data. Raising awareness of patient-defined, patient-generated data such as ODLs and is a necessary starting point for fostering patient engagement through healthy action. Necessary too are resolving uncertainties regarding the application of health privacy and security laws, and on the ability for clinicians to act on patient generated data. The technological infrastructure necessary for capturing, interpreting, displaying and storing patient generated data must be developed and made available during the health encounter and in the patients’ everyday life. Key to adoption of such models of health care based on bi-directional exchange of relevant information is a legal and policy climate that encourages (or at least does not discourage) patients and health care providers from implementing clinical workflow models similar to those explored by Project HealthDesign. In many cases, customary laws protecting health data do not apply to technology used by patients to collect, store and share health data. In cases where legal guidance exists, they often do not provide clear guidance on how to comply in circumstances where data is being shared routinely by patients. Significant technology, policy and practice workflow challenges remain; strategies to address and resolve these will be illuminated in the reports of the individual Project HealthDesign teams.
Acknowledgement Support for this research was provided by the Robert Wood Johnson Foundation through a grant from its Pioneer Portfolio. The authors acknowledge the contributions of the five Grantee teams and the Project HealthDesign National Program Office staff. We also thank Kathy Johnson, RN, PhDc and Madhusudan Rajendran for their work in synthesizing teams’ common data elements References 1. 2. 3. 4.
NAQC (2010). Guiding Principles for Patient Engagement http://www.gwumc.edu/healthsci/departments/nursing/naqc/documents/Patient_Engagement_Guiding.pdf Last accessed March 14, 2013. Project HealthDesign Supplement, J Biomedical Informatics, 2010;43(5): Supplement 1, S1-S56 PF Brennan, Valdez R. Medical Informatics. In MC Gibbons (Ed). eHealth Solutions for Healthcare Disparities, 2008, 93-108. Springer. DHHS (2012). HITPC Stage 3 Request for Comment http://www.healthit.gov/sites/default/files/draft_stage3_rfc_07_nov_12.pdf Last accessed March 13, 2013.
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