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  Posters - Connected Health Symposium...

  Dec 30, 2016

  Toward Expert Systems in Mental Health Assessment: A Computational Approach to the Face and Voice in Dyadic Patient-Doctor Interactions

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  Authors List:

  • Justin T Baker
  • Luciana Pennant
  • Tadas Baltrušaitis
  • Supriya Vijay
  • Elizabeth S Liebson
  • Dost Ongur
  • Louis-Philippe Morency

  Abstract:

  Background: Computational approaches to measure naturalistic behavior in clinical settings could provide an objective backstop for mental health assessment and disease monitoring, both of which are costly and unreliable using traditional methods. Objective: The objective of this pilot study was to determine which parts of the mental status exam could be reliably predicted by a combination of facial and vocal features extracted from a recorded interview using a combination of computer-assisted methods, in order to assess feasibility of our approach to quantify behavior for a longitudinal study of patients receiving psychiatric treatment. Methods: A total of 18 patients carrying diagnoses of schizophrenia, bipolar disorder, and related conditions were recruited from an inpatient psychiatric unit and participated in a total of 24 semi-structured interviews lasting 5-15 minutes (modeled after clinical rounds). Synchronized audio and video data were acquired from both patient and doctor during each encounter using 1080p webcams focused on the face and upper torso and cardioid headset microphones. Standardized psychiatric symptom scales was obtained after each recorded interview. Behavioral features, including facial action units (AUs), gaze, and speech characteristics (eg, prosody, pitch, tone, texture) were computed automatically using in-house and publicly available software. To predict clinical scales we trained a linear kernel support vector regressor (SVR) using features from both the entire session (ie, global mean) and each experimental epoch (eg, means during time spent alone and each individual question), leading to 15 predictors for each clinical scale item and scale totals. We used leave-one-out validation on the training data (maximizing the Pearson correlation coefficient) to determine the C parameter for the SVR models; for testing, we used leave-one-subject-out cross-validation (ie, leaving 17 participants for training/validation in each fold). Results: Providing evidence of our approach's ability to capture and quantify relevant signal that confirms or verifies clearly visible psychopathology, we found that parameters such as brow furrowing (AU4, R=0.744) and eye widening (AU5, R=–0.601) were correlated with depression measures on the BPRS. In many cases, these effects were specific to the question or experimental epoch. For instance, unusual thought content was most evident in increased frequency of brow flashes (AU2, R=0.752) and greater smile variability (R=0.656) that occurred while participants were alone in the room. Individuals with higher ratings of delusions also showed increased brow flashes in response to a question about their self confidence (R=0.739). Many relationships showed a “dose effect” with midrange scores corresponding with moderate psychopathology. Conclusions: Our experiments show that automatically detected facial action units and speech properties can be used to predict and quantify a number of psychiatric symptoms from multiple domains of psychopathology, including both mood and psychosis. We demonstrate the importance of analyzing behaviors in the appropriate context (ie, while participants are alone or prompted with a specific question) in order to optimally extract clinically relevant information from objective indices of behavior. Thus, quantitative assessment of behavior in naturalistic settings is both feasible and informative as an adjunct to traditional methods of mental status assessment.

  • iproc 2016;2(1):e44

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  Posters - Connected Health Symposium...

  Dec 30, 2016

  Technology and Dynamic Pathways: How to Improve Nursing Care, Documentation, and Efficiency

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  Authors List:

  • Yuri Ostrovsky  
  • Terry Mahan Buttaro  
  • Julie Diamond  
  • John Hayes  

  Abstract:

  Background: Approximately 60% of adverse events in skilled nursing facilities are likely preventable. This provides an opportunity to improve care and decrease costs for ill, and especially aged, patients. The primary line of defense against adverse events in these facilities are the nurses caring for these patients each day. Nurses are responsible for recognizing early warning signs of illness and preventing falls and other complications. The care of patients with multiple comorbidities requires nurses to attend to an often overwhelming set of interacting details. Objective: Our goal is to operationalize standards of care that can be implemented by nursing staff at skilled nursing and long-term care facilities in a manner complementing their natural workflow and facilitating patient interaction and shift documentation. To improve point of care patient management, nurses require a mobile solution that can guide their patient care unobtrusively. Methods: Nurses at a Boston-area 100-bed skilled nursing/long-term care facility used our solution consisting of an app on a mobile device together with a Web-based administration and reporting system. Our custom software running on an iPod Touch device implements an adaptive methodology for succinctly guiding nurses through a systematic review of systems, a physical exam, a fall risk protocol, and other assessments suitable to their roles in the nursing facility. Dynamically created checklists that prescribe appropriate and immediate nursing interventions are automatically presented to the nurses following each assessment based on the data collected. The software utilizes behavioral “nudges” to nurse-users to minimize errors and improve speed of data entry. Additionally, a custom content creation system allows for high-level abstraction of protocol logic, enabling real-time improvement and customization of complex protocol algorithms without the need for error-prone software programming. These methods facilitate automated reporting that aids the structured thought processes of providers and caregivers. Results: Pilot results were acquired using qualitative surveys and free-form interviews. Nurses reported a high facility after only an initial 15-minute training session. All nurses confirmed that having access to an efficient and mobile care facilitation device improved the likelihood of identifying patient complications. They also felt that a mobile documentation system lowered error rates by encouraging real-time documentation. Furthermore, systematic data gathering provided an improved level of documentation both in terms of comprehensiveness and clarity. Nurses claimed the system increased efficiency and lowered the overall time required for combined patient assessment and documentation. Most importantly, staff satisfaction was highly positive, encouraging continued usage of the intervention. Conclusions: Technological interventions at skilled nursing facilities, when implemented to address the point-of-care needs of nursing personnel, can positively impact quality improvement goals, empower nursing staff, and improve patient care. The suite of tools developed here enables the operationalization of standards of care with sufficient comprehensiveness to address a sufficient portion of the complexity faced in daily nursing duties. We conclude that access to real-time protocols has a highly beneficial effect on nursing care.

  • iproc 2016;2(1):e31

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  Posters - Connected Health Symposium...

  Dec 30, 2016

  Developing and Implementing an Electronic Patient-Reported Outcomes Measurement Using REDCap in Usual Care Psychiatric Settings

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  Authors List:

  • Alisa Busch  
  • Andrew Laband  
  • Alex Kos  
  • Thomas Weigel  

  Abstract:

  Background: Finding ways to feasibly and cost-efficiently measure patient-reported outcomes in psychiatric routine care settings is critical to facilitate patient engagement in care, improve patient outcomes, and assess the performance of our treatment programs. McLean Hospital is a free-standing psychiatric hospital that is part of the Partners HealthCare network and offers a full continuum of psychiatric care (inpatient, residential, partial hospital, and outpatient services) located on multiple campuses. We began an electronic patient-reported outcomes measurement program, the Clinical Measurement Initiative (CMI), in November 2010. Objective: To describe qualitatively and quantitatively the build and implementation experience of the McLean CMI program. Methods: The CMI aims to inform individual patient care, assist with quality assessment of clinical programs, and facilitate clinical research. On admission, discharge, and interim points, patients complete computerized self-assessments of validated clinical measures. The CMI uses Research Electronic Database Capture (REDCap), a free (but not open-source) secure Web application for building and managing online surveys. We designed a custom reporting module for individual patient reports to be available immediately at the point of care and an online aggregate reporting tool for clinical teams to track survey completion rates and outcomes. Clinical teams work closely with the CMI team to develop their CMI program/survey tools but receive no additional resources to accomplish survey administrations. We calculated descriptive statistics of admission and discharge survey administration and developed qualitative information about implementation “lessons learned” based on discussions with clinical teams that have implemented the CMI as part of the ongoing evaluation and monitoring of the program. Results: Over 20 programs representing 11 clinical psychiatric subpopulations have implemented the CMI to date; over 9000 episodes of care have been completed. Two programs (inpatient units) were unable to sustain the CMI in a way they found useful for clinical care and discontinued. Across active programs, 92% of admissions had admission assessments completed within 3 days of admission; 61% of discharges included a survey administration within 3 days of discharge. Clinical programs varied in the ability to successfully implement and sustain the CMI (eg, 69% of active CMI programs had >70% of admissions with admission surveys completed; over half accomplished >90% of admission surveys). Having at least one clinical champion at each program was a key driver for successful implementation. Champions served several needs: problem-solving successful new workflows, generating team enthusiasm, and setting team expectations for the importance of integrating the CMI information into clinical care. Teams that integrated the CMI into their clinical care with patients were also more successful in sustaining the CMI program. Conclusions: Achieving electronic patient reported outcomes measurement in intensive treatment psychiatric settings using REDCap and custom reporting tools is feasible but more easily accomplished in residential and partial hospital levels of care (compared to inpatient), where patient acuity is high but less severe and lengths of stay are longer. Clinical champions play critical roles in successful implementation and maintenance of electronic patient reported outcomes measurement and can be successful independent of program level of care or patient acuity.

  • iproc 2016;2(1):e45

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  Posters - Connected Health Symposium...

  Dec 30, 2016

  Searching for Infertility Information Online: Differences Between Men and Women

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  Authors List:

  • Phyllis Zelkowitz
  • Stephanie Robins
  • Paul Grunberg

  Abstract:

  Background: The Internet is an easily accessible source of medical information. People with stigmatized illnesses such as infertility often prefer to search for health-related information on the Internet; this is particularly true of men, who are in general less likely than women to seek help for medical problems and ask fewer questions and receive less information from health care providers. There has been limited research on the extent to which online resources are geared toward the information needs of men and women. Objective: Differences between men and women in Internet searches about issues related to infertility and its treatment were examined, using data from an online survey of male and female fertility patients. Methods: Patients seeking fertility care at four fertility clinics in Montreal and Toronto, Canada, were invited to participate in an anonymous online survey. Inclusion criteria included age 18 or over and the ability to answer survey questions in either English or French. Participants completed the survey either on an iPad at the time of recruitment or via a secure link, which was emailed to them. The survey included questions about whether the respondent had searched online for information about infertility, and which of a range of topics had been the subjects of an Internet search. Chi-square analysis was used to evaluate differences between male and female respondents. Results: A total of 549 people, including 245 men (44.6%) and 304 women (55.4%), completed the survey. The average age of the participants was 36.5 years (SD=5.51). Time in treatment varied from first consultation to over 5 years of treatment. Most participants (87.9%) had searched the Internet for information about infertility, with women significantly more likely to report that they had done so (93.7% of women vs 80.3% of men, P<.001). Men and women were equally likely to search for information about causes of infertility, diagnostic tests, their own diagnosis, treatment options, and success rates. However, more women than men searched the scientific literature on infertility (88.4% vs 78.7%, P=.005), sought information about their own doctor (69.6% vs 49.2%, P<.001) as well as other fertility clinics (71.3% vs 56.5%, P=.001), and used the Internet to learn about the experience of other people with infertility concerns (70.7% vs 52.3%, P<.001). Conclusions: The majority of fertility patients consulted the Internet for information. Previous research has indicated that women tend to see themselves as primarily responsible for obtaining information about infertility and its treatment. The results of the present study suggest that they use the Internet more than men to find such information, particularly as it pertains to treatment providers. Women may also be more inclined than men to seek social support via the Internet. Further study is required to determine whether male fertility patients have particular concerns and whether these are adequately addressed by available online resources.

  • iproc 2016;2(1):e37

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  Posters - Connected Health Symposium...

  Dec 30, 2016

  Mobile Apps for Behavioral Health: A Survey on User Engagement

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  Authors List:

  • Jessica M Lipschitz
  • James F Burgess Jr

  Abstract:

  Background: The number of mobile app interventions for behavioral health has grown exponentially over the last decade. A recent study by the IMS Institute for Healthcare Informatics found that health and wellness apps available to consumers more than doubled between 2013 and 2015, from 43,000 to over 90,000. Despite their availability, mobile app interventions are not yet offered or encouraged routinely in behavioral health treatment planning. One possible reason for minimal implementation is limited knowledge of patient preferences regarding use of these interventions. Objective: The current study seeks to increase knowledge in three key areas related to mobile app interventions designed specifically to address a key area of behavioral health: depression and/or anxiety. Depression and/or anxiety were selected as the focus for this project given that these mental health disorders affect over a quarter of the US population and are highly comorbid. Key research questions include (1) What are the demographics of patients who are most interested in using mobile app interventions for depression and/or anxiety? and (2) What are the reasons patients endorse for disinterest in mobile app interventions for depression and/or anxiety? Methods: Potential participants were identified at random from a list of veterans diagnosed with a mood and/or anxiety disorder (per the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition) who attended a primary care appointment during 2014 at a Veterans Affairs Medical Center located in a major metropolitan area of the northeastern United States. A total of 400 potential participants are being mailed a brief (15 minute) survey. A modified Dillman method is being used in which a prenotification letter is mailed out to participants, followed by the survey with an opt-out form, followed by up to two additional mailings for those potential participants who do not respond. Results: Data collection is expected to be complete in late August of 2016. The poster proposed here will include findings from descriptive analyses on use of technology (smart phones, mobile apps in general, mobile apps for any behavioral health concern, and mobile apps for depression and/or anxiety) in different demographics. Additionally, descriptive data on reasons endorsed for low willingness to use mobile app interventions for depression and/or anxiety (when recommended by a health care provider as well as when accessed independently) will be presented. Finally, findings from analyses evaluating the relationship between demographic variables (race, gender, education, age, and symptom severity) and level of interest in mobile app interventions for depression and/or anxiety will be presented. Conclusions: The target audience for mobile app interventions for depression and/or anxiety and reasons patients may choose not to use these interventions will be identified. Limitations include using a veteran-only sample and focusing exclusively on interventions for depression and anxiety rather than other areas of behavioral health. Future research should seek to build upon findings by broadening the scope of investigation to civilians and other areas of behavior change. Findings from this study can inform healthcare providers and app developers on patient preferences regarding mobile app interventions.

  • iproc 2016;2(1):e42

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  Posters - Connected Health Symposium...

  Dec 30, 2016

  Putting Young People at the Forefront of Their Mental Health Care Through Technology for Holistic Assessment and Routine Outcome Tracking

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  Authors List:

  • Debra Rickwood
  • Sally Bradford

  Abstract:

  Background: Young people have the highest need for mental health support yet are least likely to seek it from professional services. Enabling access and engagement in mental health care is essential to intervening early to provide appropriate and effective care to prevent escalation of young people’s mental health problems. In response, Australia initiated headspace: the National Youth Mental Health Foundation, which provides friend-to-friend (f2f) and online services to young people aged 12-25 with emerging mental health problems. Innovations in technology are being developed to better engage and put young people at the forefront of their mental health care. Objective: The aim was to investigate how technology could be used to facilitate holistic psychosocial assessments for young people entering mental health services and routinely track meaningful outcomes across an episode of care. Objectives were to determine whether this use of technology would be engaging and acceptable to young people and how it would affect appropriateness and effectiveness of the care received. Methods: An electronic holistic psychosocial assessment tool (EhHAT) and a brief routine mental health outcome measure (MyLifeTracker) were developed in collaboration with young people and their service providers. The EhHAT was piloted using a quasi-experimental 2-phase treatment-as-usual/intervention design with 339 youth and 13 clinicians. Engagement and rates of disclosure and risk across the psychosocial domains were compared in a mixed methods approach. MyLifeTracker was implemented with all clients, and quantitative data were available from 15,222 young people and used to compare MyLifeTracker with standardized mental health outcome measures. Qualitative data were collected from a small subsample to examine their experience of this routine monitoring approach. Results: The EhHAT was shown to be highly engaging and acceptable to young people and resulted in disclosure of psychosocial risks that were 2.8 through 10.4 times higher compared with the non-intervention group. MyLifeTracker was shown to have concurrent validity against well-validated measures of psychological distress, functioning, and life satisfaction. It was highly sensitive to change with excellent stability. Young people valued the opportunity to report and track their progress. For both applications, the use of technology improved the experience for young people, making them feel more in control, and providing better information for clinicians. Conclusions: The results show that technology is effective at engaging young people in their own mental health care. They are comfortable providing personal information via tablet applications and are more likely to disclose in this format. This yields better information for clinicians to provide more appropriate and tailored care. Young people appreciate the opportunity to routinely track their outcomes via technology and find this a helpful way to have conversations with their clinicians around the changes that are meaningful to them, leading to more appropriate and effective care. These holistic assessment and outcome tracking tools are essential ways forward harnessing technology to put young people at the forefront of their own mental health care.

  • iproc 2016;2(1):e40

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  Posters - Connected Health Symposium...

  Dec 30, 2016

  Patient Reported Value and Usability of a Digital Health Intervention for Asthma

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  Authors List:

  • Rajan Merchant  
  • Rubina Inamdar  
  • Kelly Henderson  
  • Meredith Barrett  
  • David Van Sickle  

  Abstract:

  Background: Digital health tools are increasingly recognized as effective in improving asthma clinical outcomes such as control and adherence; however, few studies have evaluated patient perspectives on the usability and value of these tools in supporting asthma self-management. Patient perceptions of digital health tools, including usability and satisfaction, will determine the success of these digital health interventions and the durability of their effects. Objective: We aimed to assess patients’ usability feedback and satisfaction with an asthma digital health platform after 12 months of use. Methods: We administered surveys to participants of a randomized controlled clinical study designed to measure the clinical effectiveness of the Propeller Health Asthma Platform. The electronic surveys evaluated patients’ feedback on the usability of the sensor and the perceived value of the platform and information provided after 12 months of use. The clinical study had enrolled patients (N=495) in parallel arms from specialty and primary care clinics. Intervention group patients (n=250) used electronic inhaler sensors to track the date, time and geographic location of medication use. Patients received access to a digital health platform including smartphone and Web-based applications that provided information about their asthma medication use trends, real-time asthma control, guidelines-based education, and personalized support for 12 months. Physicians could monitor the status of their patients and receive notifications about short-acting beta agonist (SABA) overuse. Survey results reported here represent adult participants from the intervention group who completed the exit survey at 12 months. Results: Respondents (n=89) reported being very satisfied (79%) or somewhat satisfied (20%) with the inhaler sensor, stating that the sensor was “small,” “unobtrusive,” and “easy to use” and carry. A total of 90% of respondents found the information they received via the platform useful, with 93% expressing satisfaction with the information. In open-ended responses, participants cited valuing how the platform increased awareness about their asthma control status and medication use, provided “relevant” and “timely” information, and identified potential environmental triggers that exacerbated their symptoms, with 65% of respondents identifying 1-7 new triggers as result of the information. Respondents described improved communication with their doctors: 46% of the respondents had talked with their doctor about the information they received, and 22% stated that their doctor recommended or changed a specific aspect of their asthma management as a result of the information. Over 50% of respondents said that they felt their asthma was more controlled as a result of the information they received, which is supported by the clinical results demonstrating 63% of uncontrolled patients achieved control during the program. Conclusions: Patients reported positive usability of a digital health platform for asthma self-management, citing that it was easy to use and fit into their lives unobtrusively. Almost all patients perceived value from the digital health platform in contributing to their self-management, finding value in increasing self-awareness, identifying asthma triggers, offering actionable information, and improving communication with their doctors. ClinicalTrial: Clinicaltrials.gov NCT01509183; https://clinicaltrials.gov/ct2/show/NCT01509183 (Archived by WebCite at http://www.webcitation.org/6nIIEejW7).

  • iproc 2016;2(1):e36

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  Posters - Connected Health Symposium...

  Dec 30, 2016

  The Impact of Electronic Administration of the MMSE on Error Rates and Data Quality

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  Authors List:

  • Dan DeBonis  
  • Todd Solomon
  • David Miller

  Abstract:

  Background: The Mini-Mental State Examination (MMSE) is a widely used measure of cognition in clinical practice and is a frequently used screening and outcome measure for cognition-enhancing clinical drug trials for Alzheimer’s Disease. Scoring and administration errors on the MMSE in a clinical drug trial can contribute to placebo response and unreliable data. Examples of such errors range from basic addition errors to failure to adhere to structured administration instructions. Prior research from independent clinical review of paper versions of the scale as part of a data quality monitoring program in worldwide studies has demonstrated that MMSE error rates among clinical trial raters tend to be upwards of 30% on initial administration. An enhanced tablet-based electronic (eCOA) version of the MMSE was developed for clinical trials by Bracket and has been utilized in worldwide clinical trials since 2013. The scale is administered in a system compliant with US Food and Drug Administration guidelines on the use of electronic medical records in trials and was designed to be equivalent to the paper MMSE. The eCOA version was augmented with internal logic, automated scoring and addition, standardized instructions, and enforcement of administration conventions. These enhancements were intended to decrease errors and increase the likelihood that the scale would be consistently administered and scored according to standard conventions. Objective: Our objectives were to (1) determine if error rates using the eCOA MMSE were reduced when compared to standard paper administration and comparable to eCOA error rates from an analysis published in 2015 and (2) consider the impact of utilizing enhanced eCOA versions of scales like the MMSE in clinical practice. Methods: All data from this analysis and comparative analyses were from multinational studies that trained and certified raters on the MMSE and included an in-study data monitoring program of clinical reviews to detect scoring and administration errors. Error rates on the enhanced electronic versions of the scales used in a late-phase Alzheimer’s study were compared to those previously seen in other studies when using paper versions and the enhanced eCOA MMSE. The dataset for this eCOA analysis was limited to English-speaking sites. Results: A total of 66/626 (10.5%) of the enhanced eCOA MMSEs reviewed at the inclusion visits contained an error in scoring which required outreach to the rater. This represents a statistically significant reduction (P<.0001) in the error rates compared to paper administration of the MMSE (877/2889, 30.4%) and is consistent with the findings from a prior analysis of an eCOA MMSE dataset from another trial (107/990,10.8%). Conclusions: Use of enhanced eCOA for the MMSE in clinical trials has significantly improved error rates and data quality compared to standard paper administration. Enhanced eCOA that is designed to minimize errors and enforce administration standards could be a useful tool to reduce common errors and increase the precision of the MMSE use in clinical practice.

  • iproc 2016;2(1):e46

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  Posters - Connected Health Symposium...

  Dec 30, 2016

  Feasibility of Using a Game-Based Cognitive Assessment for Older Adults in Emergency Care

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  Authors List:

  • Tiffany Tong  
  • Chelsea DeGuzman  
  • Mark Chignell  
  • Mary Catherine Tierney  
  • Jacques Lee  

  Abstract:

  Background: Developed nations are currently facing a demographic shift towards a higher proportion of elderly people and increasing age-related health concerns. Elderly adults often enter into the health care system through emergency services. As a result, emergency departments (EDs) are an opportune environment for cognitive screening to improve patient outcomes. Existing methods are not designed for use in EDs and require administration by a trained test administrator. As an alternative, we suggest serious games as screening tools that can be self-administered or used with minimal assistance in emergency care. The use of validated and reliable serious games can encourage more efficient and engaging cognitive screening. Objective: Our objective was to investigate the feasibility and usability issues identified in the process of evaluating the validity and reliability of a game-based cognitive screening tool administered on an interactive tablet, for use by elderly adults in emergency care. Methods: We carried out a study in a hospital ED to evaluate the validity of the serious game with elderly adults over the age of 70 years (N=146). Validity was assessed in terms of the correlation of game performance with outcomes of current assessment methods. Comparative assessments included the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and the Confusion Assessment Method (CAM). We also assessed the reliability of our game-based assessment tool in a hospital ED for a large subset of the patients who underwent a revised protocol involving follow-up sessions at a minimum of every 8 hours (n=127). In both studies, research personnel (RP) administered the standard cognitive assessments and then asked the patients to play the serious game. Recruitment for the reliability study overlapped with our validation study, with 56 potential overlap cases. Results: In our validation group, 141 of 146 patients consented to play the serious game. Performance on the serious game correlated significantly with the MoCA (r=–0.339, P<.001), MMSE (r=–0.558, P<.001), CAM (r=0.565, P<.001), and other cognitive assessments. In our reliability study of 127 adults, we obtained data from 126 people. We observed good test-retest reliability with Pearson correlation r values between 0.5 and 0.7. 
These results supplement findings from our validation study that the assessment was correlated with MMSE, MoCA, and CAM scores. Usability feedback suggested that the game was too easy and the need to use a stylus for users with dry skin. All patients were able to play the serious game administered on a tablet either independently or with minimal assistance from RP. In the latter case, RP would assist by holding the tablet as some patients found it heavy. Conclusions: This research demonstrates the feasibility, validity, and reliability of using a game-based cognitive assessment in a clinical setting. Elderly users in an ED can use our screening tool by improving on the usability of the system by incorporating their feedback in our design process. This research shows that appropriately designed serious games can be self-administered or used with minimal assistance in a clinical setting repeatedly for each person.

  • iproc 2016;2(1):e35

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  Posters - Connected Health Symposium...

  Dec 30, 2016

  A Comprehensive Survey of Managed Care Organization (MCO) Medication Adherence Intervention Programs

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  Authors List:

  • Clifford Jones  
  • Fawad Piracha  
  • Kenny Ng  
  • Ian Sullivan  
  • Michael Boice  
  • David Coutts  
  • Stefanie Mazlish  
  • Aishwarya Nagarian
  • Slanix Paul Alex
  • Sudarshan Phani
  • Kalee Shah  
  • Aneesh Sheth
  • Kamila Sip  
  • Tracey Van Kempen  
  • Upal Basu Roy  

  Abstract:

  Background: Medication adherence is defined as the extent to which patients takes their medications as prescribed by their healthcare providers. Medication non-adherence is understood to be a costly and dangerous problem. In the United States alone, non-adherence is estimated to incur US $290 billion in avoidable healthcare expenditures each year. On an individual basis, annual spending for non-adherent patients with hypertension and diabetes is approximately US $4000 greater than for patients who are adherent to their medications. In addition, non-adherent patients suffering from heart failure are hospitalized 2.5 times more frequently than those who adhere to their medication regimens, resulting in a profoundly diminished quality of life, while driving up health care costs. This problem will continue to get worse as more patients will continue to take multiple medications to treat chronic conditions. While medication non-adherence is a serious problem, its origins are complex and not fully elucidated. Some of the more commonly accepted causes include complex treatment regimens, adverse drug reactions, forgetfulness, socioeconomic issues, health literacy, and personal beliefs. Objective: The purpose of this study is map the current landscape of managed care organization (MCO) medication adherence programs, review the common medication adherence interventions that are used by MCOs, and identify the need for more enhanced intervention targeting such as predictive analytics platforms and other non-predictive methodologies. Methods: This research survey was conducted in the spring of 2014 and involved 30 MCOs. Each MCO was asked about their current medication adherence intervention programs. Each MCO was represented by an employee who manages medication adherence programs within the organization. Each employee was interviewed during a 10-week long period by virtue of a detailed questionnaire that involved open-ended and multiple-choice questions. Questions addressed information about each MCO’s existing medication adherence programs and interventions. Information and insight into the effectiveness of existing interventions, the criteria by which patients are selected for interventions, and the processes by which adherence is measured by each MCO were summarized for this survey. The questionnaire was divided into three basic components: general questions regarding existing medication adherence programs, types of interventions used and their individual effectiveness, and how patients are selected to receive interventions. Respondents also discussed any unique services their programs provide and any future plans of expansion. All data was recorded by 5 interviewers and then reanalyzed by the authors. All 30 MCOs interviewed have medication adherence intervention programs that target their patient population. The MCOs vary in size and coverage demographics. A total of 19 of the organizations are classified as small MCOs (enrollment <200,000), while 11 are classified as large MCOs (enrollment >200,000). The surveyed MCO populations encompass most of the continental United States as well as Puerto Rico. The coverage demographics of these MCOs include commercial, Medicare, and/or Medicaid. Overall, 53% of MCOs cover a commercial population, 80% cover a Medicare population, and 27% cover a Medicaid population. The highest coverage combinations are commercial and Medicare and Medicare and Medicaid, which account for 23% of the population and 17% of the population, respectively. Results: Most MCO medication adherence program interventions are directed at patients with chronic cardiovascular disease states (diabetes, hypertension, hyperlipidemia, and heart failure). Cardiovascular diseases are a primary concern for MCOs because of the chronic use of medications/therapies that are associated with the management of such disease states. Furthermore, there is a strong association with medication non-adherence and increased hospitalization rates. Most MCOs use triggers and retrospective adherence measures to select patients for interventions. MCOs seem to follow a rule-based approach (using specific demographic profiles and predefined events to trigger interventions) rather than treating each patient individually. One way to implement a more personalized approach is through predictive analytics. While only 7% of MCOs currently use predictive analytics, over half of the surveyed MCOs plan on incorporating some type of analytics platform. Many are interested in adopting a platform that identifies interventions most likely to engage patients and influence their behavior, avoiding wasteful spending on interventions with patients who will not need them. These enhanced programs are dynamic and self-learning and can rapidly adapt to new intervention techniques. MCOs focus on four intervention channels to improve patient adherence: telephone outreach, direct mail, provider-centric, and face-to-face visits. These interventions are conducted through a combination of in-house and outsourced techniques. MCOs perceive current approaches as only moderately effective because of a failure to intervene before patients are non-adherent and a failure to personalize interventions. This relative ineffectiveness of current interventions has been implicated in previous studies. Conclusions: The study results suggest that most MCO medication adherence programs target chronic, comorbid cardiovascular disease patients through a system of triggers and retrospective adherence measures. Most MCOs intervene using telephone outreach, direct mail, provider-centric, and face-to-face visits through a combination of in-house and outsourced methods. This approach is seen as only moderately effective as it fails to personalize interventions and intervene before a patient becomes non-adherent. In light of these findings, predictive analytics platforms can play an increasing role in addressing the needs and shortcomings of existing MCO medication adherence programs.

  • iproc 2016;2(1):e28

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  Posters - Connected Health Symposium...

  Dec 30, 2016

  Visual Food Diary for Social Support, Dietary Changes and Weight Loss

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  Authors List:

  • Heli Salmenius-Suominen  
  • Mikko Lehtovirta  
  • Henna Vepsäläinen  
  • Hanna Konttinen  
  • Maijaliisa Erkkola  

  Abstract:

  Background: To find out to what degree health-improving dietary behavior (eg, increasing consumption of vegetables and fruits) can be furthered in virtual peer support groups moderated by a nutrition professional using a mobile app. Objective: To find out, to what degree health improving dietary behaviour, for example increasing the use of vegetables and fruits can be furthered in virtual peer support groups moderated by a nutrition professional using a mobile application. Methods: In this feasibility study, volunteering adult men and women (body mass index [BMI]>25 kg/m²) were recruited both from a diabetes outpatient clinic and from an occupational health care unit. Participants were divided into 3 groups. All participants used a smartphone app that allowed them to keep a visual food journal, share their meals and activity with group members, and receive virtual coaching from a nutrition professional. Outcomes were assessed via surveys at baseline, after the intervention, and 4 weeks later using a food frequency questionnaire (FFQ). Frequency of app use, weight, and waist circumference were estimated at baseline and after the intervention. Results: Mean weight loss (n=25) after intervention was 1.5 kg (95% CI 0.79 to 2.29), or 1.7% (95% CI 0.89 to 2.5) in all subjects together, and 1.5% in group 1 (95% CI –0.02 to 2.9), 1.9% in group 2 (95% CI 0.56 to 3.25), and 1.7% in group 3 (95% CI –0.20 to 3.61), respectively. Mean waist circumference (n=22) reduced 2.4% (95 % CI 1.3 to 3.4). At the end of the 4-week intervention, the consumption of vegetables and fruits (n=26) had increased by 55%, while the consumption of sweets and chocolate had decreased by 39% as compared to baseline. Almost all participants (84%) strongly agreed (40%) or agreed (44%) that they got support and encouragement from other group members. Similarly, altogether 92% of participants strongly agreed (67%) or agreed (25%) that they felt like they belonged to the group. The engagement level was high, with the average user uploading 5.2 meals a day and recording a total of 9.3 sessions a day. There was some variation between the 3 groups. On average, users in groups 1, 2, and 3 uploaded 3.8 (n=8, 862 meals), 5.8 (n=8, 1315 meals) and 5.7 (n=11, 1774 meals) meals a day, respectively, within the 4 week period. In total, the participants uploaded 3951 meals and recorded 7066 sessions. Conclusions: Smartphone-based virtual peer support can be used as a tool to promote healthy eating both in outpatient clinic and occupational health settings.

  • iproc 2016;2(1):e38

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  Posters - Connected Health Symposium...

  Dec 30, 2016

  Using High-Tech and High-Touch Methods for Effective Population Health Management

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  Authors List:

  • Sashi Padarthy
  • Cristina Crespo
  • Keri Rich
  • Nagaraja Srivatsan

  Abstract:

  Background: Healthcare consumers today are more engaged and empowered than ever before. Social, mobile, and in-home self-monitoring tools now enable health consumers to manage their health conditions and receive advice, support, and care without leaving home. Consumers are increasingly buying biometric devices such the Fitbit and Apple Watch to monitor their health. Many payers and employers are providing fitness devices to encourage consumers to be healthy, often offering discounts to their healthcare premiums. These devices create a sense of euphoria initially, but they do not yield sustainable health results and necessary behavioral changes in consumers to sustain it in the long-term. According to BJ Fogg, Stanford professor, sustainable behavior change (B) is a function of the motivation of the individual (M) × ability of the individual to do the task (A) × Trigger (T); B=MAT.   Objective: Our hypothesis was that self-managed digital triggers alone would not yield sustainable change, but a combination of digital biometrics devices that automatically monitor health supplemented by weekly contact with a health coach would deliver sustainable changes to consumer behavior. Methods: During the 6-month pilot in 2014 and 12-month program in 2015, Cognizant wanted to test this hypothesis by providing appropriate triggers to motivate individuals to achieve their goals and ensure sustainable change in behavior. We provided Fitbit devices to participants and shared their digital information with our health coach. The coach worked with the individuals in setting individual goals, reviewed their progress, and helped with better food and exercise choices. The coach also used gamification to motivate the group into friendly competitions. Results: At the end of the pilot, participants averaged 70,000 steps per week. This was well above the average US baseline of 36,000 steps and Fitbit average of 42,000 steps. Pilot participants also lost over an average of 10 pounds each. However, during the pilot we determined coaching needed more focus on incremental, sustainable goals to lead toward long-term behavior changes. During a 6 month period between the pilot ending and the 2015 program initiation where pilot participants did not have access to a coach, participants’ activity level decreased, and weight lost during the pilot was gained back. During the 2015 program, participants sustained on average 66,000 steps per week (well above both the US and Fitbit average). Obese 1 participants normalized steps at 70,000 per week and increased activity from baseline almost 30%. This group started at the very bottom of “Somewhat Active” and transitioned into the “Active” category. Obese 2 participants normalized steps at 60,000 per week and increased activity from baseline almost 33%, which transitioned them from “Low Active” to “Somewhat Active.” This was the largest increase by any group from baseline. Our control group, who did not have access to coaches, averaged only 43,000 steps per week. Conclusions: Coach influence and participant change is most effective in the first 6 months. After that, coaches concentrate on incrementally increasing activity in a sustainable manner. Effective population health management and high value personalized services can be achieved by weight loss from increasing physical activity and better food choices brought about by direct consumer engagement that combines “high tech” with “high touch.”

  • iproc 2016;2(1):e17

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