BACKGROUND:Patients with advanced cancer often experience immense cancer pain that negatively impacts their quality of life. Interventions to address cancer-related pain are limited. METHODS:We conducted a randomized trial of a digital therapeutic app (ePAL) for patients with advanced cancer receiving care in a specialty palliative care clinic at a tertiary care hospital. Patients were randomized to ePAL or usual care. ePAL included 1) active pain monitoring; 2) artificial intelligence algorithm to triage patient symptoms; and 3) patient education to address barriers to pain management. Participants were instructed to use ePAL over eight weeks. Patient-reported pain symptoms were assessed at baseline, Week-4, and Week-8 (primary endpoint) using the Brief Pain Inventory. Secondary outcomes include pain-related hospitalizations by Week-8. RESULTS:We enrolled 112 patients who were randomly assigned to ePAL (N = 56) or usual care (N = 56). Patients utilized ePAL on average 2.1 times per week to report pain symptoms, and 47.6% reported their pain at least once per week over eight weeks. Patients randomized to ePAL reported lower pain scores at Week-4 (mean: 3.16 vs. 4.28, P = 0.010) and week-8 (mean:2.99 vs. 4.05, P = 0.017), compared to those receiving usual care. Participants randomized to ePAL were less likely to experience a pain-related hospitalization compared to those in the usual care group (7.1% vs. 23.2% P = 0.018) CONCLUSIONS: ePAL was associated with lower patient-reported pain and fewer pain-related hospitalizations compared to usual care in patients with advanced cancer. This study demonstrates the promise of digital therapeutics for improving patients' symptoms while reducing burdensome hospitalizations.

Background: It is well established that lack of physical activity is detrimental to the overall health of an individual. Modern-day activity trackers enable individuals to monitor their daily activities to meet and maintain targets. This is expected to promote activity encouraging behavior, but the benefits of activity trackers attenuate over time due to waning adherence. One of the key approaches to improving adherence to goals is to motivate individuals to improve on their historic performance metrics. Objective: The aim of this work was to build a machine learning model to predict an achievable weekly activity target by considering (1) patterns in the user's activity tracker data in the previous week and (2) behavior and environment characteristics. By setting realistic goals, ones that are neither too easy nor too difficult to achieve, activity tracker users can be encouraged to continue to meet these goals, and at the same time, to find utility in their activity tracker. Methods: We built a neural network model that prescribes a weekly activity target for an individual that can be realistically achieved. The inputs to the model were user-specific personal, social, and environmental factors, daily step count from the previous 7 days, and an entropy measure that characterized the pattern of daily step count. Data for training and evaluating the machine learning model were collected over a duration of 9 weeks. Results: Of 30 individuals who were enrolled, data from 20 participants were used. The model predicted target daily count with a mean absolute error of 1545 (95% CI 1383-1706) steps for an 8-week period. Conclusions: Artificial intelligence applied to physical activity data combined with behavioral data can be used to set personalized goals in accordance with the individual's level of activity and thereby improve adherence to a fitness tracker; this could be used to increase engagement with activity trackers. A follow-up prospective study is ongoing to determine the performance of the engagement algorithm.

BACKGROUND The uptake of digital health technology (DHT) has been surprisingly low in clinical practice. Despite showing great promise to improve patient outcomes and disease management, there is limited information on the factors that contribute to the limited adoption of DHT, particularly for hypertension management. OBJECTIVE This scoping review provides a comprehensive summary of barriers to and facilitators of DHT adoption for hypertension management reported in the published literature with a focus on provider- and patient-related barriers and facilitators. METHODS This review followed the methodological framework developed by Arskey and O’Malley. Systematic literature searches were conducted on PubMed or Medical Literature Analysis and Retrieval System Online, Cumulative Index to Nursing and Allied Health Literature, and Excerpta Medica database. Articles that reported on barriers to and/or facilitators of digital health adoption for hypertension management published in English between 2008 and 2017 were eligible. Studies not reporting on barriers or facilitators to DHT adoption for management of hypertension were excluded. A total of 2299 articles were identified based on the above criteria after removing duplicates, and they were assessed for eligibility. Of these, 2165 references did not meet the inclusion criteria. After assessing 134 studies in full text, 98 studies were excluded (full texts were either unavailable or studies did not fulfill the inclusion criteria), resulting in a final set of 32 articles. In addition, 4 handpicked articles were also included in the review, making it a total of 36 studies. RESULTS A total of 36 studies were selected for data extraction after abstract and full-text screening by 2 independent reviewers. All conflicts were resolved by a third reviewer. Thematic analysis was conducted to identify major themes pertaining to barriers and facilitators of DHT from both provider and patient perspectives. The key facilitators of DHT adoption by physicians that were identified include ease of integration with clinical workflow, improvement in patient outcomes, and technology usability and technical support. Technology usability and timely technical support improved self-management and patient experience, and positive impact on patient-provider communication were most frequently reported facilitators for patients. Barriers to use of DHTs reported by physicians include lack of integration with clinical workflow, lack of validation of technology, and lack of technology usability and technical support. Finally, lack of technology usability and technical support, interference with patient-provider relationship, and lack of validation of technology were the most commonly reported barriers by patients. CONCLUSIONS Findings suggest the settings and context in which DHTs are implemented and individuals involved in implementation influence adoption. Finally, to fully realize the potential of digitally enabled hypertension management, there is a greater need to validate these technologies to provide patients and providers with reliable and accurate information on both clinical outcomes and cost effectiveness.

Background It is well reported that tracking physical activity can lead to sustained exercise routines, which can decrease disease risk. However, most stop using trackers within a couple months of initial use. The reasons people stop using activity trackers can be varied and personal. Understanding the reasons for discontinued use could lead to greater acceptance of tracking and more regular exercise engagement. Objective The aim of this study was to determine the individualistic reasons for nonengagement with activity trackers. Methods Overweight and obese participants (n=30) were enrolled and allowed to choose an activity tracker of their choice to use for 9 weeks. Questionnaires were administered at the beginning and end of the study to collect data on their technology use, as well as social, physiological, and psychological attributes that may influence tracker use. Closeout interviews were also conducted to further identify individual influencers and attributes. In addition, daily steps were collected from the activity tracker. Results The results of the study indicate that participants typically valued the knowledge of their activity level the activity tracker provided, but it was not a sufficient motivator to overcome personal barriers to maintain or increase exercise engagement. Participants identified as extrinsically motivated were more influenced by wearing an activity tracker than those who were intrinsically motivated. During the study, participants who reported either owning multiple technology devices or knowing someone who used multiple devices were more likely to remain engaged with their activity tracker. Conclusions This study lays the foundation for developing a smart app that could promote individual engagement with activity trackers.

Background Type 1 diabetes mellitus (T1DM) is characterized by chronic insulin deficiency and consequent hyperglycemia. Patients with T1DM require long-term exogenous insulin therapy to regulate blood glucose levels and prevent the long-term complications of the disease. Currently, there are no effective algorithms that consider the unique characteristics of T1DM patients to automatically recommend personalized insulin dosage levels. Objective The objective of this study was to develop and validate a general reinforcement learning (RL) framework for the personalized treatment of T1DM using clinical data. Methods This research presents a model-free data-driven RL algorithm, namely Q-learning, that recommends insulin doses to regulate the blood glucose level of a T1DM patient, considering his or her state defined by glycated hemoglobin (HbA1c) levels, body mass index, engagement in physical activity, and alcohol usage. In this approach, the RL agent identifies the different states of the patient by exploring the patient’s responses when he or she is subjected to varying insulin doses. On the basis of the result of a treatment action at time step t, the RL agent receives a numeric reward, positive or negative. The reward is calculated as a function of the difference between the actual blood glucose level achieved in response to the insulin dose and the targeted HbA1c level. The RL agent was trained on 10 years of clinical data of patients treated at the Mass General Hospital. Results A total of 87 patients were included in the training set. The mean age of these patients was 53 years, 59% (51/87) were male, 86% (75/87) were white, and 47% (41/87) were married. The performance of the RL agent was evaluated on 60 test cases. RL agent–recommended insulin dosage interval includes the actual dose prescribed by the physician in 53 out of 60 cases (53/60, 88%). Conclusions This exploratory study demonstrates that an RL algorithm can be used to recommend personalized insulin doses to achieve adequate glycemic control in patients with T1DM. However, further investigation in a larger sample of patients is needed to confirm these findings.

Hypertension is a major risk factor for stroke, cardiovascular disease, and end-stage renal disease, and its prevalence is expected to rise dramatically. Effective hypertension management is thus critical. A particular priority is decreasing the incidence of uncontrolled hypertension. Early identification of patients at risk for uncontrolled hypertension would allow targeted use of personalized, proactive treatments. We develop machine learning models (logistic regression and recurrent neural networks) to stratify patients with respect to the risk of exhibiting uncontrolled hypertension within the coming three-month period. We trained and tested models using EHR data from 14,407 and 3,009 patients, respectively. The best model achieved an AUROC of 0.719, outperforming the simple, competitive baseline of relying prediction based on the last BP measure alone (0.634). Perhaps surprisingly, recurrent neural networks did not outperform a simple logistic regression for this task, suggesting that linear models should be included as strong baselines for predictive tasks using EHR

Background Participant recruitment, especially for frail, elderly, hospitalized patients, remains one of the greatest challenges for many research groups. Traditional recruitment methods such as chart reviews are often inefficient, low-yielding, time consuming, and expensive. Best Practice Alert (BPA) systems have previously been used to improve clinical care and inform provider decision making, but the system has not been widely used in the setting of clinical research. Objective The primary objective of this quality-improvement initiative was to develop, implement, and refine a silent Best Practice Alert (sBPA) system that could maximize recruitment efficiency. Methods The captured duration of the screening sessions for both methods combined with the allotted research coordinator hours in the Emerald-COPD (chronic obstructive pulmonary disease) study budget enabled research coordinators to estimate the cost-efficiency. Results Prior to implementation, the sBPA system underwent three primary stages of development. Ultimately, the final iteration produced a system that provided similar results as the manual Epic Reporting Workbench method of screening. A total of 559 potential participants who met the basic prescreen criteria were identified through the two screening methods. Of those, 418 potential participants were identified by both methods simultaneously, 99 were identified only by the Epic Reporting Workbench Method, and 42 were identified only by the sBPA method. Of those identified by the Epic Reporting Workbench, only 12 (of 99, 12.12%) were considered eligible. Of those identified by the sBPA method, 30 (of 42, 71.43%) were considered eligible. Using a side-by-side comparison of the sBPA and the traditional Epic Reporting Workbench method of screening, the sBPA screening method was shown to be approximately four times faster than our previous screening method and estimated a projected 442.5 hours saved over the duration of the study. Additionally, since implementation, the sBPA system identified the equivalent of three additional potential participants per week. Conclusions Automation of the recruitment process allowed us to identify potential participants in real time and find more potential participants who meet basic eligibility criteria. sBPA screening is a considerably faster method that allows for more efficient use of resources. This innovative and instrumental functionality can be modified to the needs of other research studies aiming to use the electronic medical records system for participant recruitment.

BACKGROUND:Fever is an important vital sign and often the first one to be assessed in a sick child. In acutely ill children, caregivers are expected to monitor a child's body temperature at home after an initial medical consult. Fever literacy of many caregivers is known to be poor, leading to fever phobia. In children with a serious illness, the responsibility of periodically monitoring temperature can add substantially to the already stressful experience of caring for a sick child.OBJECTIVE:The objective of this pilot study was to assess the feasibility of using the iThermonitor, an automated temperature measurement device, for continuous temperature monitoring in postoperative and postchemotherapy pediatric patients.METHODS:We recruited 25 patient-caregiver dyads from the Pediatric Surgery Department at the Massachusetts General Hospital (MGH) and the Pediatric Cancer Centers at the MGH and the Dana Farber Cancer Institute. Enrolled dyads were asked to use the iThermonitor device for continuous temperature monitoring over a 2-week period. Surveys were administered to caregivers at enrollment and at study closeout. Caregivers were also asked to complete a daily event-monitoring log. The Generalized Anxiety Disorder-7 item questionnaire was also used to assess caregiver anxiety at enrollment and closeout.RESULTS:Overall, 19 participant dyads completed the study. All 19 caregivers reported to have viewed temperature data on the study-provided iPad tablet at least once per day, and more than a third caregivers did so six or more times per day. Of all participants, 74% (14/19) reported experiencing an out-of-range temperature alert at least once during the study. Majority of caregivers reported that it was easy to learn how to use the device and that they felt confident about monitoring their child's temperature with it. Only 21% (4/9) of caregivers reported concurrently using a device other than the iThermonitor to monitor their child's temperature during the study. Continuous temperature monitoring was not associated with an increase in caregiver anxiety.CONCLUSIONS:The study results reveal that the iThermonitor is a highly feasible and easy-to-use device for continuous temperature monitoring in pediatric oncology and surgery patients.TRIAL REGISTRATION:ClinicalTrials.gov NCT02410252; https://clinicaltrials.gov/ct2/show/NCT02410252 (Archived by WebCite at http://www.webcitation.org/73LnO7hel).

Many health care organizations use value-based care strategies that include population health management programs and data analytics to stratify their population and identify high-risk and high-cost patients. Most of these programs target the top 5% most expensive patients. However, little is known about these patients prior to reaching the top 5% of cost, or how their characteristics change over time. To address these gaps, we analyzed the differences in characteristics of patients from 3 different cost segments over 5 years (2011-2015). To evaluate potentially avoidable hospitalizations and associated savings in the health care cost of older patients using Personal Emergency Response Service (PERS). We conducted a retrospective, longitudinal, multicenter study to evaluate potentially avoidable hospitalizations of 2643 older patients over 5 years (2011-2015). All patients had at least one inpatient and/or outpatient encounter, and at least one episode of home health care during the study period. Additionally, all patients used PERS at home anytime during the study period. We ranked patients by their annual health care cost and then grouped them into the following segments for each respective year: T-segment constitutes the top 5% most expensive patients; M-segment comprises the middle 45% of patients; B-segment includes the bottom 50% least expensive patients. We then evaluated differences in the characteristics of patients in the B-, M- and T-cost segments in each study year. Continuous variables were compared by t test (two-tailed) for normally distributed variables and Kruskal-Wallis Rank Sum test for skewed variables. The chi-square test was used for categorical variables. The three cost segments differed significantly each year (P<.05) with respect to: demographics (age, education), PERS utilization (Incidents, ER transport), health care utilization (hospitalizations, length of stay, 30-, 90-, and 180-day readmissions, outpatient encounters) and medical conditions (number of conditions, Charlson Comorbidity Index). Further, we analyzed the number of potentially avoidable hospitalizations (as defined by CMS) and associated cost savings in each segment. All hospitalizations occurred among patients in the T- and M-segments while the B-segment was hospitalization-free each year. The percentage of avoidable hospitalizations in the M-segment compared with the T-segment was 3 times greater (75% vs 25%, P<.001). While the potential cost saving from avoidable hospitalizations in the entire population increased from $3.0M to $8.2M (2011-2015), the majority of these cost savings were in the M-segment compared with the T-segment (60% vs 40%, P<.001). Although many health care organizations target intensive and costly interventions to their most expensive patients, this analysis suggests there is untapped potential to control costs and improve care beyond focusing on the highest cost patients. Namely, targeting patients in the middle cost segment may offer great opportunity for population management programs to maximize both potentially avoidable hospitalizations and cost savings.

Heart failure is one of the leading causes of hospitalization in the United States. Advances in big data solutions allow for storage, management, and mining of large volumes of structured and semi-structured data, such as complex healthcare data. Applying these advances to complex healthcare data has led to the development of risk prediction models to help identify patients who would benefit most from disease management programs in an effort to reduce readmissions and healthcare cost, but the results of these efforts have been varied. The primary aim of this study was to develop a 30-day readmission risk prediction model for heart failure patients discharged from a hospital admission.

Despite widespread adoption and demonstrated value in a range of industries, machine learning predictive algorithms are yet to be routinely used in frontline medical care. Significant health system and industry-based resources are allocated towards validating and refining predictive algorithms for a range of applications to ensure accuracy and reliability. For these algorithms to be useful and useable, further work is required to understand how and why they might fit into, and augment existing clinical workflows. This qualitative study assessed the value and usability of a novel machine learning technology to predict and explain the risk of 30-day hospital readmission in patients with heart failure (HF). It involved exploring opportunities for integration of the technology within existing clinical workflows, and investigating key roles that use current readmission risk scores and may use future scores. Semi-structured interviews (n=27) and targeted observations (n=3) were carried out with key stakeholders, including physicians, nurses, hospital administration, and non-clinical support staff. Participants were recruited from cardiology and general medicine units at an academic medical center within the Partners HealthCare system. Data was analyzed via inductive thematic and workflow analysis. Findings were validated via member checking across limited key roles (n=3). Results highlighted a number of factors that were deemed necessary by staff for successful integration of a risk prediction tool into existing clinical workflow. These included, but were not limited to the following. Staff clearly stated that any new tool must be easily accessible from within the electronic health record, which dictates the majority of existing clinical workflow. Staff emphasized that information should be consistently accurate and that any display must be digestible efficiently, intuitively and quickly (ie, within <5 seconds). Additionally, staff discussed that outputs of the risk prediction tool must match their clinical intuition, experience and interactions with the patient. To be truly valuable, the tool must also provide added value over and above these factors: some staff indicated that provision of role-specific and actionable next steps based on the system output would provide novel value to their daily work. Using these considerations, a number of role groups were identified as potentially able to derive value from the proposed risk prediction tool, including case managers, attending RNs, responding clinicians, hospital administration staff, nursing directors and attending physicians. Acceptability and value varied by role, specialization and clinical context. For example, cardiology-trained clinicians reported feeling well-versed in providing good clinical care and minimizing preventable readmissions, and thus saw less value in the tool. General medicine staff, however, indicated that a HF-specific tool may be impractical for their day-to-day work given the range of clinical presentations seen by them. Findings resonate with existing literature around successful implementation and adoption of technologies in health care. Frontline clinicians are incredibly discerning around proposed changes to their existing workflow. Many HF readmission risk tools and initiatives have been trialled with mixed success; frontline staff demonstrated fatigue around piloting new initiatives. However, given the right conditions, staff reported some perceived value in machine learning-based tools to improve their daily work.

BACKGROUND:Soaring health care costs and a rapidly aging population, with multiple comorbidities, necessitates the development of innovative strategies to deliver high-quality, value-based care.OBJECTIVE:The goal of this study is to evaluate the impact of a risk assessment system (CareSage) and targeted interventions on health care utilization.METHODS:This is a two-arm randomized controlled trial recruiting 370 participants from a pool of high-risk patients receiving care at a home health agency. CareSage is a risk assessment system that utilizes both real-time data collected via a Personal Emergency Response Service and historical patient data collected from the electronic medical records. All patients will first be observed for 3 months (observation period) to allow the CareSage algorithm to calibrate based on patient data. During the next 6 months (intervention period), CareSage will use a predictive algorithm to classify patients in the intervention group as "high" or "low" risk for emergency transport every 30 days. All patients flagged as "high risk" by CareSage will receive nurse triage calls to assess their needs and personalized interventions including patient education, home visits, and tele-monitoring. The primary outcome is the number of 180-day emergency department visits. Secondary outcomes include the number of 90-day emergency department visits, total medical expenses, 180-day mortality rates, time to first readmission, total number of readmissions and avoidable readmissions, 30-, 90-, and 180-day readmission rates, as well as cost of intervention per patient. The two study groups will be compared using the Student t test (two-tailed) for normally distributed and Mann Whitney U test for skewed continuous variables, respectively. The chi-square test will be used for categorical variables. Time to event (readmission) and 180-day mortality between the two study groups will be compared by using the Kaplan-Meier survival plots and the log-rank test. Cox proportional hazard regression will be used to compute hazard ratio and compare outcomes between the two groups.RESULTS:We are actively enrolling participants and the study is expected to be completed by end of 2018; results are expected to be published in early 2019.CONCLUSIONS:Innovative solutions for identifying high-risk patients and personalizing interventions based on individual risk and needs may help facilitate the delivery of value-based care, improve long-term patient health outcomes and decrease health care costs.TRIAL REGISTRATION:ClinicalTrials.gov NCT03126565; https://clinicaltrials.gov/ct2/show/NCT03126565 (Archived by WebCite at http://www.webcitation.org/6ymDuAwQA).

Maximizing the interseizure period is one of the treatment goals in epilepsy. Factors associated with changes in the interseizure period have not been adequately studied.The objective of this study is to identify factors associated with change in mean interseizure period (MIP) in frequently admitted epilepsy patients. We reviewed clinical notes from the electronic medical records of 303 epilepsy patients who received care at the Partners Healthcare system between 2013-2016 and had 2 or more inpatient encounters within 365 days of the index date. MIP was defined as the average duration between two seizure episodes. The outcome of interest was the change in the MIP between the index date and the 365th day following it. Two reviewers independently determined the MIP. Models were adjusted for age, gender, education, seizure type, Charlson Comorbidity Index Score, encounters with a neurologist, AED type and number of AEDs at index. 86 incident and 217 prevalent epilepsy patients were included. For incident patients, the mean and median change in MIP was 174 days and 179 days, respectively. Every additional year of age was associated with an MIP higher by 2 days (p<0.01). Compared to focal seizures, a mixed seizure type was associated with MIP lower by 102 days (p<0.001). Compared to sodium channel blockers, SV2A binding agents were associated with MIP higher by 132 days (p<0.05). For prevalent patients, the mean and median change in MIP was 30 days and 0 days, respectively. Compared to those who switched their AED type, going from a single generation AED to mixed generation AEDs was associated with 105 less seizure-free days (p<0.01). Among high cost epilepsy patients, change in MIP was associated with age, seizure type and AED characteristics. Efforts to further characterize this population and interventions designed to improve this outcome are needed.

BACKGROUND Heart failure (HF) patients have a high readmission rate with approximately 20% of patients being readmitted within 30-days after discharge. Hospital interventions to reduce HF readmissions are resource- and effort-intensive. Widespread availability of electronic medical record data has spurred interest in using machine learning-based techniques for risk stratification of heart failure patients. The predictive performance of machine learning-based predictive models is often evaluated solely using the Area Under the Receiver Operating Characteristic (AUROC) curve. However, the AUROC is independent of prevalence therefore predictive models with the same AUROC can have differential clinical utility. Furthermore, the AUROC does not provide any insight about the presence of overfitting or decay in predictive performance of a model over time, both of which can affect its real-world performance. OBJECTIVE Our primary objective is to assess real-world performance of a 30-day readmission risk prediction model for HF patients, which had an AUROC of 0.71 in the training dataset. METHODS Predictions for risk of 30-day readmissions in HF patients in the Partners Healthcare System were prospectively obtained from the model. We assessed the positive (PPV) and negative predictive value (NPV), in addition to sensitivity, specificity, accuracy, model calibration and Brier score. RESULTS Four hundred twenty index admissions that were not part of the training dataset were included in this prospective evaluation. Readmission rate was 24% (101 30-day readmissions). The AUROC of the predictive model was 0.57. At a discrimination threshold of 0.2 for flagging high-risk index admissions, the sensitivity and specificity of the model were 53.46% and 63.32%, respectively. The PPV and NPV were 31.57% and 81.12%, respectively. The Brier score was 0.19. CONCLUSIONS Our analysis offers important insights about the real-world performance of this predictive model. The NPV suggests that the model’s prediction about patients at low risk for readmission are reliable. This insight can be useful in optimizing resource allocation for patients with heart failure.

April 27, 2018April 10, 2018Free AccessDrivers of Epilepsy-related Cost of Care in an Integrated Healthcare System (P6.280)Sujay Kakarmath, Mahesh Agarwal, Sara Golas, Jennifer Felsted, Joseph Dye, Jesse Fishman, Marjory Levey, and Stephen AgboolaAuthors Info & AffiliationsApril 10, 2018 issue90 (15_supplement)https://doi.org/10.1212/WNL.90.15_supplement.P6.280 Letters to the Editor

Physical inactivity is one of the leading risk factors contributing to rising rates of chronic diseases and has been associated with deleterious health outcomes in patients with chronic disease conditions. FeatForward is a mobile phone app designed to encourage patients with cardiometabolic risk (CMR) factors to increase their levels of physical activity. To evaluate the effect of the FeatForward mobile phone app on physical activity levels (primary outcome) and global CMR factors (secondary outcomes) in patients with chronic conditions. In this 6-month, 2-arm randomized controlled trial, adult participants endorsing at least 1 study-eligible condition (obesity, [pre-]diabetes, [pre-]hypertension) were enrolled and assigned to either the intervention group (FeatForward app and standard care) or control group (standard care only). The primary and secondary outcomes were, respectively, change from baseline in physical activity (step count) and CMR factors (weight, body mass index [BMI], waist circumference, glycated hemoglobin [HbA1c], fasting blood glucose, systolic/diastolic blood pressures, serum lipids, C-reactive protein [CRP]). CMR data were collected at 3 time-points: baseline, 3 months, and 6 months. Step count data were recorded continuously by patients’ study-issued activity trackers and collected in batches at 3 and 6 months. At study end, patients’ weekly average step counts (WAS) were calculated as total steps taken divided by days of step data (0-7) for each of 26 study weeks. Mixed-effects linear regression models evaluated change over time between groups for the primary outcome and secondary outcomes. All models controlled for baseline values. The step count model additionally controlled for proportion of days without data, defined as (7 – days of data) / 7. Analyses were conducted for both groups overall, and by disease cohort (obesity, diabetes, hypertension). Step count and CMR data were analyzed for 128 intervention and 133 control patients. There were no demographic differences between groups. While there was an overall downward trend in WAS for both groups, the intervention group decreased significantly less than the control group, with a slope of -29.3 steps per week compared to controls’ -57.9 (P=.02). Intervention patients with obesity slightly increased their step count overtime, differing significantly from controls (slope of 0.9 vs -90.2; P<.001). Intervention patients significantly lowered their BMI per study month compared to controls (slopes -0.23 vs -0.02; P=.04). Additionally, intervention patients with hypertension significantly decreased weight (P=.003), BMI (P=.002), and CRP (P=.03) per month compared to the control group. Waist circumference, HbA1c, fasting blood glucose, blood pressure, and lipids did not differ significantly by group or disease cohort over time. While it is common for patient engagement with physical activity trackers to decrease over the course of a study, patients using the FeatFoward app had a slower decline in physical activity compared to controls. Intervention patients experienced a reduction in their BMI from a mean of 34.3 to 33.4, compared to controls’ 34.8 to 35.0. Patients with hypertension experienced significant decreases in BMI, weight, and CRP compared to controls. Future analyses will evaluate the impact of app engagement levels on step counts and CMR factors for the intervention group.

Half of Medicare reimbursement goes toward caring for the top 5% of the most expensive patients. However, little is known about these patients prior to reaching the top or how their costs change annually. To address these gaps, we analyzed patient flow and associated health care cost trends over 5 years. To evaluate the cost of health care utilization in older patients by analyzing changes in their long-term expenditures. This was a retrospective, longitudinal, multicenter study to evaluate health care costs of 2643 older patients from 2011 to 2015. All patients had at least one episode of home health care during the study period and used a personal emergency response service (PERS) at home for any length of time during the observation period. We segmented all patients into top (5%), middle (6%-50%), and bottom (51%-100%) segments by their annual expenditures and built cost pyramids based thereon. The longitudinal health care expenditure trends of the complete study population and each segment were assessed by linear regression models. Patient flows throughout the segments of the cost acuity pyramids from year to year were modeled by Markov chains. Total health care costs of the study population nearly doubled from US $17.7M in 2011 to US $33.0M in 2015 with an expected annual cost increase of US $3.6M (P=.003). This growth was primarily driven by a significantly higher cost increases in the middle segment (US $2.3M, P=.003). The expected annual cost increases in the top and bottom segments were US $1.2M (P=.008) and US $0.1M (P=.004), respectively. Patient and cost flow analyses showed that 18% of patients moved up the cost acuity pyramid yearly, and their costs increased by 672%. This was in contrast to 22% of patients that moved down with a cost decrease of 86%. The remaining 60% of patients stayed in the same segment from year to year, though their costs also increased by 18%. Although many health care organizations target intensive and costly interventions to their most expensive patients, this analysis unveiled potential cost savings opportunities by managing the patients in the lower cost segments that are at risk of moving up the cost acuity pyramid. To achieve this, data analytics integrating longitudinal data from electronic health records and home monitoring devices may help health care organizations optimize resources by enabling clinicians to proactively manage patients in their home or community environments beyond institutional settings and 30- and 60-day telehealth services.

Atrial fibrillation (AFib) is the most common form of heart arrhythmia and a potent risk factor for stroke. Nonvitamin K antagonist oral anticoagulants (NOACs) are routinely prescribed to manage AFib stroke risk; however, nonadherence to treatment is a concern. Additional tools that support self-care and medication adherence may benefit patients with AFib.The aim of this study was to evaluate the perceived usability and usefulness of a mobile app designed to support self-care and treatment adherence for AFib patients who are prescribed NOACs.A mobile app to support AFib patients was previously developed based on early stage interview and usability test data from clinicians and patients. An exploratory pilot study consisting of naturalistic app use, surveys, and semistructured interviews was then conducted to examine patients' perceptions and everyday use of the app.A total of 12 individuals with an existing diagnosis of nonvalvular AFib completed the 4-week study. The average age of participants was 59 years. All participants somewhat or strongly agreed that the app was easy to use, and 92% (11/12) reported being satisfied or very satisfied with the app. Participant feedback identified changes that may improve app usability and usefulness for patients with AFib. Areas of usability improvement were organized by three themes: app navigation, clarity of app instructions and design intent, and software bugs. Perceptions of app usefulness were grouped by three key variables: core needs of the patient segment, patient workflow while managing AFib, and the app's ability to support the patient's evolving needs.The results of this study suggest that mobile tools that target self-care and treatment adherence may be helpful to AFib patients, particularly those who are newly diagnosed. Additionally, participant feedback provided insight into the varied needs and health experiences of AFib patients, which may improve the design and targeting of the intervention. Pilot studies that qualitatively examine patient perceptions of usability and usefulness are a valuable and often underutilized method for assessing the real-world acceptability of an intervention. Additional research evaluating the AFib Connect mobile app over a longer period, and including a larger, more diverse sample of AFib patients, will be helpful for understanding whether the app is perceived more broadly to be useful and effective in supporting patient self-care and medication adherence.

CORA is a personalized smartphone-based self-management app designed to help cancer patients on oral anti-cancer medications manage medication, medication side-effects, and symptoms with the overall goal of improving their quality of life. To evaluate the effect of CORA on quality of life in patients on oral anti-cancer medications. Eighty-four patients were randomized to either an intervention group that received CORA plus usual care or a control group that received usual care. Quality of life was measured using the Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F) scale administered at enrollment, 6 and 12 weeks. Engagement with the app was assessed by determining the unique days using the app. We evaluated the effect of engagement on FACIT-F both as a continuous variable (days using the app) and as a categorical outcome (low, medium, and high). Group differences for all outcomes over the study period were assessed using repeated measures mixed model analysis. Relative to the control group, the intervention group improved FACIT-F by 0.36 (95% CI 0.10-0.61) P=.006 per week over the study period. As a continuous variable, each additional day using the app was significantly associated with an improved FACIT-F score per week in the study [0.0060 (95% CI -0.000034-0.012), P=.05]. Within the intervention group that used the app, those who were most engaged with the app were significantly more likely to improve their quality of life over the study relative to the least engaged group [0.37 per week (95% CI 0.19-1.94), P=.05]. CORA may have significantly improved quality of life (FACIT-F) in cancer patients over 12 weeks. Smartphone applications may positively impact health and behavioral outcomes in cancer patients on oral anti-cancer medications.