Implementation and Evaluation of a mHealth-Based Community Health Worker Feedback Loop for Hispanics with and at Risk for Diabetes.

BACKGROUND: Gaps in accessibility and communication hinder diabetes care in poor communities. Combining mobile health (mHealth) and community health workers (CHWs) into models to bridge these gaps has great potential but needs evaluation. OBJECTIVE: To evaluate a mHealth-based, Participant-CHW-Clinician feedback loop in a real-world setting. DESIGN: Quasi-experimental feasibility study with intervention and usual care (UC) groups. PARTICIPANTS: A total of 134 participants (n = 67/group) who were all low-income, uninsured Hispanics with or at-risk for type 2 diabetes. INTERVENTION: A 15-month study with a weekly to semimonthly mHealth Participant-CHW-Clinician feedback loop to identify participant issues and provide participants monthly diabetes education via YouTube. MAIN MEASURES: We used pre-defined feasibility measures to evaluate our intervention: (a) implementation, the execution of feedback loops to identify and resolve participant issues, and (b) efficacy, intended effects of the program on clinical outcomes (baseline to 15-month HbA1c, systolic blood pressure (SBP), diastolic blood pressure (DBP), and weight changes) for each group and their subgroups (at-risk; with diabetes, including uncontrolled (HbA1c ≥ 7%)). KEY RESULTS: CHWs identified 433 participant issues (mean = 6.5 ± 5.3) and resolved 91.9% of these. Most issues were related to supplies, 26.3% (n = 114); physical health, 23.1% (n = 100); and medication access, 20.8% (n = 90). Intervention participants significantly improved HbA1c (- 0.51%, p = 0.03); UC did not (- 0.10%, p = 0.76). UC DBP worsened (1.91 mmHg, p < 0.01). Subgroup analyses revealed HbA1c improvements for uncontrolled diabetes (intervention: - 1.59%, p < 0.01; controlled: - 0.72, p = 0.03). Several variables for UC at-risk participants worsened: HbA1c (0.25%, p < 0.01), SBP (4.05 mmHg, p < 0.01), DBP (3.21 mmHg, p = 0.01). There were no other significant changes for either group. CONCLUSIONS: A novel mHealth-based, Participant-CHW-Clinician feedback loop was associated with improved HbA1c levels and identification and resolution of participant issues. UC individuals had several areas of clinical deterioration, particularly those at-risk for diabetes, which is concerning for progression to diabetes and disease-related complications. CLINICAL TRIAL: NCT03394456, accessed at https://clinicaltrials.gov/ct2/show/NCT03394456.

Implementation of patient priorities-aligned care in a home-based primary care program.

BACKGROUND: Older adults may be limited in their ability to access care that meets their health goals owing to disease burden, financial instability, and psychosocial barriers. A home-based primary care (HBPC) program established in 2020 within a large family medicine practice uses the Patient Priorities Care (PPC) approach to identify and address patients' health priorities. When incorporated as part of the HBPC model of care, the PPC approach has the potential to enhance person-centered care for older adults in a way that best supports their health goals. OBJECTIVE: The objective of this study is to summarize common recommendations for alignment of care with patients' health outcome goals after implementation of the PPC approach in an HBPC population. METHODS: This retrospective study was exempt from review by an institutional review board. After enrollment in the HBPC program, patients participated in a PPC priorities identification conversation to identify their health outcome goals and care preferences. Through chart review, 2 researchers independently categorized these goals based on the set of values they most reflect: connecting, managing health, enjoying life, and functioning. Aspects of care in place before enrollment in HBPC were considered to determine any adjustments that needed to be made to align care with patients' identified priorities. RESULTS: The most common value associated with patients' most desired health outcome goal was functioning (n = 33, 66%). For secondary and tertiary health outcome goals, the most common value identified was managing health (secondary, n = 28, 56%; tertiary, n = 22, 44%). Common recommendations made to align care with patients' identified priorities included stopping potentially harmful medications, starting medications for untreated conditions, starting physical or occupational therapy, and adjusting medications. CONCLUSION: Through the PPC approach, patients' values were identified and care was assessed to aid in attainment of individualized health outcome goals and tailor care to What Matters most.

Long-Term Effectiveness of the TIME Intervention to Improve Diabetes Outcomes in Low-Income Settings: a 2-Year Follow-Up.

BACKGROUND: We previously found that a 6-month multidimensional diabetes program, TIME (Telehealth-Supported, Integrated Community Health Workers, Medication-Access) resulted in improved clinical outcomes. OBJECTIVE: To follow TIME participant clinical outcomes for 24 months PARTICIPANTS: Low-income Latino(a)s with type 2 diabetes DESIGN AND INTERVENTION: We collected post-intervention clinical data for five cohorts (n = 101, mean n = 20/cohort) who participated in TIME programs from 2018 to 2020 in Houston, Texas. MAIN MEASURES: We gathered HbA1c (primary outcome), weight, body mass index (BMI), and blood pressure data at baseline, 6 months (intervention end), and semiannually thereafter until 24 months after baseline to assess sustainability. We also evaluated participant loss to follow-up until 24 months. KEY RESULTS: Participants decreased HbA1c levels during the intervention (p < 0.0001) and maintained these improvements at each timepoint from baseline to 24 months (p range: < 0.0001 to 0.015). Participants reduced blood pressure levels during TIME and maintained these changes at each timepoint from baseline until 18 months (systolic p range < 0.0001 to 0.0005, diastolic p range: < 0.0001 to 0.008) but not at 24 months (systolic: p = 0.065; diastolic: p = 0.85). There were no significant weight changes during TIME or post-intervention: weight (p range = 0.07 to 0.77), BMI (p range = 0.11 to 0.71). Attrition rates (loss to follow-up during the post-intervention period) were 5.9% (6 months), 24.8% (12 months), 35.6% (18 months), and 41.8% (24 months). CONCLUSIONS: It is possible for vulnerable populations to maintain long-term glycemic and blood pressure improvements using a multiple dimensional intervention. Attrition rates rose over time but show promise given the majority of post-intervention timepoints occurred during the COVID-19 pandemic when low-income populations were most susceptible to suboptimal healthcare access. Future studies are needed to evaluate longitudinal outcomes of diabetes interventions conducted by local clinics rather than research teams.

Digital Biomarkers of Cognitive Frailty: The Value of Detailed Gait Assessment Beyond Gait Speed.

BACKGROUND: Cognitive frailty (CF), defined as the simultaneous presence of cognitive impairment and physical frailty, is a clinical symptom in early-stage dementia with promise in assessing the risk of dementia. The purpose of this study was to use wearables to determine the most sensitive digital gait biomarkers to identify CF. METHODS: Of 121 older adults (age = 78.9 ± 8.2 years, body mass index = 26.6 ± 5.5 kg/m2) who were evaluated with a comprehensive neurological exam and the Fried frailty criteria, 41 participants (34%) were identified with CF and 80 participants (66%) were identified without CF. Gait performance of participants was assessed under single task (walking without cognitive distraction) and dual task (walking while counting backward from a random number) using a validated wearable platform. Participants walked at habitual speed over a distance of 10 m. A validated algorithm was used to determine steady-state walking. Gait parameters of interest include steady-state gait speed, stride length, gait cycle time, double support, and gait unsteadiness. In addition, speed and stride length were normalized by height. RESULTS: Our results suggest that compared to the group without CF, the CF group had deteriorated gait performances in both single-task and dual-task walking (Cohen's effect size d = 0.42-0.97, p < 0.050). The largest effect size was observed in normalized dual-task gait speed (d = 0.97, p < 0.001). The use of dual-task gait speed improved the area under the curve (AUC) to distinguish CF cases to 0.76 from 0.73 observed for the single-task gait speed. Adding both single-task and dual-task gait speeds did not noticeably change AUC. However, when additional gait parameters such as gait unsteadiness, stride length, and double support were included in the model, AUC was improved to 0.87. CONCLUSIONS: This study suggests that gait performances measured by wearable sensors are potential digital biomarkers of CF among older adults. Dual-task gait and other detailed gait metrics provide value for identifying CF above gait speed alone. Future studies need to examine the potential benefits of gait performances for early diagnosis of CF and/or tracking its severity over time.

Sentiment Analysis of Insomnia-Related Tweets via a Combination of Transformers Using Dempster-Shafer Theory: Pre- and Peri-COVID-19 Pandemic Retrospective Study.

BACKGROUND: The COVID-19 pandemic has imposed additional stress on population health that may result in a change of sleeping behavior. OBJECTIVE: In this study, we hypothesized that using natural language processing to explore social media would help with assessing the mental health conditions of people experiencing insomnia after the outbreak of COVID-19. METHODS: We designed a retrospective study that used public social media content from Twitter. We categorized insomnia-related tweets based on time, using the following two intervals: the prepandemic (January 1, 2019, to January 1, 2020) and peripandemic (January 1, 2020, to January 1, 2021) intervals. We performed a sentiment analysis by using pretrained transformers in conjunction with Dempster-Shafer theory (DST) to classify the polarity of emotions as positive, negative, and neutral. We validated the proposed pipeline on 300 annotated tweets. Additionally, we performed a temporal analysis to examine the effect of time on Twitter users' insomnia experiences, using logistic regression. RESULTS: We extracted 305,321 tweets containing the word insomnia (prepandemic tweets: n=139,561; peripandemic tweets: n=165,760). The best combination of pretrained transformers (combined via DST) yielded 84% accuracy. By using this pipeline, we found that the odds of posting negative tweets (odds ratio [OR] 1.39, 95% CI 1.37-1.41; P<.001) were higher in the peripandemic interval compared to those in the prepandemic interval. The likelihood of posting negative tweets after midnight was 21% higher than that before midnight (OR 1.21, 95% CI 1.19-1.23; P<.001). In the prepandemic interval, while the odds of posting negative tweets were 2% higher after midnight compared to those before midnight (OR 1.02, 95% CI 1.00-1.07; P=.008), they were 43% higher (OR 1.43, 95% CI 1.40-1.46; P<.001) in the peripandemic interval. CONCLUSIONS: The proposed novel sentiment analysis pipeline, which combines pretrained transformers via DST, is capable of classifying the emotions and sentiments of insomnia-related tweets. Twitter users shared more negative tweets about insomnia in the peripandemic interval than in the prepandemic interval. Future studies using a natural language processing framework could assess tweets about other types of psychological distress, habit changes, weight gain resulting from inactivity, and the effect of viral infection on sleep.

Using Wearable Sensors to Measure Goal Achievement in Older Veterans with Dementia.

Aligning treatment with patients' self-determined goals and health priorities is challenging in dementia care. Wearable-based remote health monitoring may facilitate determining the active participation of individuals with dementia towards achieving the determined goals. The present study aimed to demonstrate the feasibility of using wearables to assess healthcare goals set by older adults with cognitive impairment. We present four specific cases that assess (1) the feasibility of using wearables to monitor healthcare goals, (2) differences in function after goal-setting visits, and (3) goal achievement. Older veterans (n = 17) with cognitive impairment completed self-report assessments of mobility, then had an audio-recorded encounter with a geriatrician and wore a pendant sensor for 48 h. Follow-up was conducted at 4-6 months. Data obtained by wearables augments self-reported data and assessed function over time. Four patient cases illustrate the utility of combining sensors, self-report, notes from electronic health records, and visit transcripts at baseline and follow-up to assess goal achievement. Using data from multiple sources, we showed that the use of wearable devices could support clinical communication, mainly when patients, clinicians, and caregivers work to align care with the patient's priorities.

Testing the Impact of a Collaborative, Goal-Setting, and Behavioral Telehealth Intervention on Diabetes Distress: A Randomized Clinical Trial.

Background:Diabetes distress is underrecognized and associated with poor outcomes. This study tested whether a 12-month collaborative, goal-setting, and behavioral telehealth intervention reduced diabetes distress levels.Methods:This is a secondary analysis of the Healthy Outcomes through Patient Empowerment (HOPE) study that included individuals (N = 225) with uncontrolled diabetes and depression living at least 20 miles from a Veteran's Affairs medical center. Participants were randomized to HOPE (intervention) or Enhanced Usual Care (EUC) with education. We evaluated diabetes distress levels as measured by the Problem Areas in Diabetes (PAID) Questionnaire and its four subscales (emotional, diabetes management, social, and treatment distress) at baseline, 6, and 12 months.Results:Between-group analysis revealed greater improvements in HOPE versus EUC for: 6-month PAID total score (p = 0.04), emotional (p = 0.03), and social (p = 0.04) subscales; 12-month PAID total score (p = 0.07) and emotional subscale (p = 0.07). Within-group comparisons showed larger effect sizes for HOPE compared with EUC: 12-month PAID total scores (0.82 vs. 0.54), 6-month emotional burden (0.54 vs. 0.31), and 6-month (0.32 vs. 0.08) and 12-month (0.41 vs. 0.12) social burdens. Repeated-measures analysis evaluating treatment group and time trended toward improvement in PAID overall for HOPE compared with EUC participants, but was not statistically significant (ß = 6.96; SE = 4.35; p = 0.13).Discussion:Clinically meaningful reductions in PAID overall and the emotional and social subscales were observed in HOPE compared with EUC participants.Conclusion:Further evaluation of diabetes telehealth interventions that include other facets related to diabetes distress, including treatment, diabetes management, social, and emotional burdens, is warranted. Clinical Trial Number. NCT01572389; Clinical Trial Registry. https://clinicaltrials.gov/ct2/show/NCT01572389.

A Telehealth-supported, Integrated care with CHWs, and MEdication-access (TIME) Program for Diabetes Improves HbA1c: a Randomized Clinical Trial.

BACKGROUND: Many individuals with diabetes live in low- or middle-income settings. Glycemic control is challenging, particularly in resource-limited areas that face numerous healthcare barriers. OBJECTIVE: To compare HbA1c outcomes for individuals randomized to TIME, a Telehealth-supported, Integrated care with CHWs (Community Health Workers), and MEdication-access program (intervention) versus usual care (wait-list control). DESIGN: Randomized clinical trial. PARTICIPANTS: Low-income Latino(a) adults with type 2 diabetes. INTERVENTIONS: TIME consisted of (1) CHW-participant telehealth communication via mobile health (mHealth) for 12 months, (2) CHW-led monthly group visits for 6 months, and (3) weekly CHW-physician diabetes training and support via telehealth (video conferencing). MAIN MEASURES: Investigators compared TIME versus control participant baseline to month 6 changes of HbA1c (primary outcome), blood pressure, body mass index (BMI), weight, and adherence to seven American Diabetes Association (ADA) standards of care. CHW assistance in identifying barriers to healthcare in the intervention group were measured at the end of mHealth communication (12 months). KEY RESULTS: A total of 89 individuals participated. TIME individuals compared to control participants had significant HbA1c decreases (9.02 to 7.59% (- 1.43%) vs. 8.71 to 8.26% (- 0.45%), respectively, p = 0.002), blood pressure changes (systolic: - 6.89 mmHg vs. 0.03 mmHg, p = 0.023; diastolic: - 3.36 mmHg vs. 0.2 mmHg, respectively, p = 0.046), and ADA guideline adherence (p < 0.001) from baseline to month 6. At month 6, more TIME than control participants achieved > 0.50% HbA1c reductions (88.57% vs. 43.75%, p < 0.001). BMI and weight changes were not significant between groups. Many (54.6%) TIME participants experienced > 1 barrier to care, of whom 91.7% had medication issues. CHWs identified the majority (87.5%) of barriers. CONCLUSIONS: TIME participants resulted in improved outcomes including HbA1c. CHWs are uniquely positioned to identify barriers to care particularly related to medications that may have gone unrecognized otherwise. Larger trials are needed to determine the scalability and sustainability of the intervention. CLINICAL TRIAL: NCT03394456, accessed at https://clinicaltrials.gov/ct2/show/NCT03394456.

A Wrist-Worn Sensor-Derived Frailty Index Based on an Upper-Extremity Functional Test in Predicting Functional Mobility in Older Adults.

INTRODUCTION: Preoperative frailty is an independent risk factor for postoperative complications across surgical specialties. Functional mobility such as gait, timed up and go (TUG), and 5 times sit-to-stand (5-STS) are popular preoperative frailty measurements but are not suitable for patients with severe mobility impairment. A wrist-worn sensor-derived frailty index based on an upper-extremity functional test (20-s repetitive elbow flexion-extension task; UEFI) was developed previously; however, its association with functional mobility remained unexplored. We aimed to investigate the predictive power of the UEFI in predicting functional mobility. METHODS: We examined correlation between the UEFI and gait speed, TUG duration, and 5-STS duration in 100 older adults (≥ 65 years) using multivariate regression analysis. The UEFI was calculated using slowness, weakness, exhaustion, and flexibility of the sensor-based 20-s repetitive elbow flexion-extension task. RESULTS: The UEFI was a significant predictor for gait speed and TUG duration and 5-STS duration (all R ≥ 0.60; all p < 0.001) with the variance (adjusted R2) of 35-37% for the dependent variables. The multivariate regression analysis revealed significant associations between the UEFI and gait speed (ß = -0.84; 95% confidence interval [95% CI] = [-1.19, -0.50]; p < 0.001) and TUG duration (ß = 16.2; 95% CI = [9.59, 22.8]; p < 0.001) and 5-STS duration (ß = 33.3; 95% CI = [23.6, 43.2]; p < 0.001), found after accounting for confounding variables (e.g., age and fear of falling scale). CONCLUSIONS: Our findings suggest that the UEFI can be performed with a wrist-worn sensor and has been validated with other established measures of preoperative frailty. The UEFI can be applied in a wide variety of patients, regardless of mobility limitations, in an outpatient setting.

Hip Fracture Risk among Hemodialysis-Dependent Patients Prescribed Opioids and Gabapentinoids.

BACKGROUND: Despite opioids' known association with hip fracture risk in the general population, they are commonly prescribed to patients with ESKD. Whether use of opioids or gabapentinoids (also used to treat pain in patients with ESKD) contributes to hip fracture risk in patients with ESKD on hemodialysis remains unknown. METHODS: In a case-control study nested within the US Renal Data System, we identified all hip fracture events recorded among patients dependent on hemodialysis from January 2009 through September 2015. Eligible cases were risk-set matched on index date with ten eligible controls. We required >1 year of Medicare Parts A and B coverage and >3 years of part D coverage to study cumulative longer-term exposure. To examine new, short-term exposure, we selected individuals with >18 months of Part D coverage and no prior opioid or gabapentinoid use between 18 and 7 months before index. We used conditional logistic regression to estimate unadjusted and multivariable-adjusted odds ratios (ORs) and 95% confidence intervals (95% CI). RESULTS: For the longer-term analyses, we identified 4912 first-time hip fracture cases and 49,120 controls. Opioid use was associated with increased hip fracture risk (adjusted OR, 1.39; 95% CI, 1.26 to 1.53). Subgroups of low, moderate, and high use yielded adjusted ORs of 1.33 (95% CI, 1.20 to 1.47), 1.53 (95% CI, 1.36 to 1.72), and 1.66 (95% CI, 1.45 to 1.90), respectively. The association with hip fractures was also elevated with new, short-term use (adjusted OR, 1.38; 95% CI, 1.25 to 1.52). There were no associations between gabapentinoid use and hip fracture. CONCLUSIONS: Among patients dependent on hemodialysis in the United States, both short-term and longer-term use of opioid analgesics were associated with hip fracture events.

Digital Biomarker Representing Frailty Phenotypes: The Use of Machine Learning and Sensor-Based Sit-to-Stand Test.

Since conventional screening tools for assessing frailty phenotypes are resource intensive and unsuitable for routine application, efforts are underway to simplify and shorten the frailty screening protocol by using sensor-based technologies. This study explores whether machine learning combined with frailty modeling could determine the least sensor-derived features required to identify physical frailty and three key frailty phenotypes (slowness, weakness, and exhaustion). Older participants (n = 102, age = 76.54 ± 7.72 years) were fitted with five wearable sensors and completed a five times sit-to-stand test. Seventeen sensor-derived features were extracted and used for optimal feature selection based on a machine learning technique combined with frailty modeling. Mean of hip angular velocity range (indicator of slowness), mean of vertical power range (indicator of weakness), and coefficient of variation of vertical power range (indicator of exhaustion) were selected as the optimal features. A frailty model with the three optimal features had an area under the curve of 85.20%, a sensitivity of 82.70%, and a specificity of 71.09%. This study suggests that the three sensor-derived features could be used as digital biomarkers of physical frailty and phenotypes of slowness, weakness, and exhaustion. Our findings could facilitate future design of low-cost sensor-based technologies for remote physical frailty assessments via telemedicine.

Decrease in Mobility during the COVID-19 Pandemic and Its Association with Increase in Depression among Older Adults: A Longitudinal Remote Mobility Monitoring Using a Wearable Sensor.

BACKGROUND: Social isolation during COVID-19 may negatively impact older adults' wellbeing. To assess its impact, we measured changes in physical activity and sleep among community-dwelling older adults, from pre-to post-pandemic declaration. METHOD: Physical activity and sleep in older adults (n = 10, age = 77.3 ± 1.9 years, female = 40%) were remotely assessed within 3-month pre-to 6-month post-pandemic declaration using a pendant-wearable system. Depression was assessed pre-and post-pandemic declaration using the Center for Epidemiologic Studies Depression scale and was compared with 48 h continuous physical activity monitoring data before and during pandemic. RESULTS: Compared to pre-pandemic, post-pandemic time spent in standing declined by 32.7% (Cohen's d = 0.78, p < 0.01), walking by 52.2% (d = 1.1, p < 0.01), step-counts by 55.1% (d = 1.0, p = 0.016), and postural transitions by 44.6% (d = 0.82, p = 0.017) with increase in sitting duration by 20.5% (d = 0.5, p = 0.049). Depression symptoms increased by 150% (d = 0.8, p = 0.046). Interestingly, increase in depression was significantly correlated with unbroken-prolong sitting bout (ρ = 0.677, p = 0.032), cadence (ρ = -0.70, p = 0.024), and sleep duration (ρ = -0.72, p = 0.019). CONCLUSION: This is one of the early longitudinal studies highlighting adverse effect of the pandemic on objectively assessed physical activity and sleep in older adults. Our observations showed need for timely intervention to mitigate hard to reverse consequences of decreased physical activity such as depression.

Caring for Unbefriended Older Adults and Adult Orphans: A Clinician Survey.

Objectives: Unbefriended older adults are those who lack the capacity to make medical decisions and do not have a completed advance directive that can guide treatment decisions or a surrogate decision maker. Adult orphans are those who retain medical decision-making capacity but are at risk of becoming unbefriended due to lack of a completed advance health care directive and lack of a surrogate decision maker. In a follow-up to the 2016 American Geriatrics Society (AGS) position statement on unbefriended older adults, we examined clinicians' experiences in caring for unbefriended older adults and adult orphans.Methods: Clinicians recruited through the AGS (N = 122) completed an online survey about their experiences with unbefriended older adults regarding the perceived frequency of contact, clinical concerns, practice strategies, and terminology; and also with adult orphans regarding the perceived frequency of contact, methods of identification, and terminology.Results: Almost all inpatient (95.9%) and outpatient (86.4%) clinicians in this sample encounter unbefriended older adults at least quarterly and 92.2% of outpatient clinicians encounter adult orphans at least quarterly. Concerns about safety (95.9%), medication self-management (90.4%), and advance care planning (86.3%) bring unbefriended older adults to outpatient clinicians' attention "sometimes" to "frequently." Prolonged hospital stays (87.7%) and delays in transitioning to end-of-life care (85.7%) bring unbefriended older adults to inpatient clinicians' attention "sometimes" to "frequently." Clinicians apply a wide range of practice strategies to these populations. Participants suggested alternative terminology to replace "unbefriended" and "adult orphan."Conclusions: This study suggests that unbefriended older adults are frequently encountered in geriatrics practice, both in the inpatient and outpatient settings, and that there is widespread awareness of adult orphans in the outpatient setting. Clinicians' awareness of both groups suggests avenues for intervention and prevention.Clinical Implications: Health care professionals in geriatric settings will likely encounter older adults in need of advocates. Clinicians, attorneys, and policymakers should collaborate to improve early detection and to meet the needs of this vulnerable population.

Integrated Model for Patient-Centered Advanced Liver Disease Care.

Advanced liver disease (AdvLD) is a high-risk common condition with a progressive, highly morbid, and often fatal course. Despite effective treatments, there are substantial shortfalls in access to and use of evidence-based supportive and palliative care for AdvLD. Although patient-centered, chronic illness models that integrate early supportive and palliative care with curative treatments hold promise, there are several knowledge gaps that hinder development of an integrated model for AdvLD. We review these evidence gaps. We also describe a conceptual framework for a patient-centered approach that explicates key elements needed to improve integrated care. An integrated model of AdvLD would allow clinicians, patients, and caregivers to work collaboratively to identify treatments and other healthcare that best align with patients' priorities.

A Novel Surgical Patient Engagement Model: A Qualitative Study of Postoperative Patients.

BACKGROUND: Strong patient engagement is often associated with better postoperative outcomes and reduced risk of dangerous and expensive complications for the patient. Our goal with this project is to define a new model specifically for surgical patient engagement to guide future work to improve patient outcomes. METHODS: Open-ended qualitative interviews were conducted with 38 postoperative patients, analyzed using the conventional content analysis method, and coded with NVivo 11. Patients from either a safety net or private hospital in the Houston area between the ages of 18 and 70 y were recruited after surgery for either thyroid, parathyroid, colon, or rectal cancer, inflammatory bowel disease, and diverticulitis. Pregnant and incarcerated patients in addition to those with postoperative complications or interview time frames greater than 4 wk postoperatively were excluded. RESULTS: Of patients completing the Patient Activation Measure, 98% obtained a score of 3 or 4, indicating optimal levels of activation despite differences in socio-economic status. Upon analysis of coded transcripts, four main themes of "self-efficacy," "resilience," "transitional agency," and "enabling agency," in addition to a fifth emergency rescue activator, "family and social support," were discovered as "drivers" of patient engagement. CONCLUSIONS: A novel model of patient engagement specific to surgical patients is necessary because of the unique recovery track they endure. Our new model can be used to develop interventions for these patients to improve their engagement and thereby their outcomes.

Telemedicine Training and Support for Community Health Workers: Improving Knowledge of Diabetes.

Background: Community health workers (CHWs) are a well-established source to improve patient health care, yet their training and support remain suboptimal. This limits program expansion and potentially compromises patient safety. The objective of the study was to evaluate the feasibility and acceptability of weekly training and support by telemedicine (videoconferencing). Materials and Methods: CHWs (n = 6) who led diabetes group visits for low-income Latinos met weekly with a health care professional for training and support. Feasibility and acceptability outcome measures included telemedicine usability, knowledge of diabetes (baseline to 6 months), and program satisfaction. Results: Telemedicine training and support were found to be feasible and acceptable as measured by usability (Telehealth Usability Questionnaire: average 4.7/5.0, ±0.4), knowledge (Diabetes Knowledge Test: pretest 15.8 ± 1.3, posttest 21.8 ± 1.2, p < 0.001, respectively), and satisfaction (Texas Department of State Health Services survey: average 5.8/6.0, ±0.5). All CHWs preferred telemedicine to in-person training. Conclusions: Telemedicine is a feasible and acceptable modality to train and support CHWs.

Examining changes in interprofessional attitudes associated with virtual interprofessional training.

Interprofessional care is essential in healthcare, but prior work has shown that physicians and nurses tend to have different perceptions about working interprofessionally (interprofessional attitudes). Although training has been shown to improve interprofessional attitudes, providing traditional face to face training is logistically challenging in the healthcare setting. The current study examined whether a virtual interprofessional training program could improve interprofessional attitudes for nurses and physicians. Among a sample of 35 physicians and nurses, results suggested that engagement in a virtual interprofessional training program was associated with improvements in interprofessional attitudes (i.e., perceived ability to work with, value in working with, and comfort in working with other professions) (p = .002), with attitudes improving an average of 0.25 points on a six-point scale (Cohen's d = 0.52). As a secondary aim, results showed that the magnitude of change in interprofessional attitudes did not differ significantly between physicians and nurses. Altogether, results suggest that virtual interprofessional training appears to be a suitable way to begin to improve interprofessional attitudes for both physicians and nurses.

Cancer-related cognitive impairment and associated factors in a sample of older male oral-digestive cancer survivors.

OBJECTIVE: This study examines the demographic and clinical variables associated with cancer-related cognitive impairment (CRCI) in a sample of older, male, oral-digestive cancer survivors at VA Medical Centers in Boston and Houston. METHODS: A two-time point, longitudinal design was used, with cognitive assessment conducted at 6 and 18 months post-diagnosis. Using ANCOVA, the cognitive functioning of 88 older adults with head and neck, esophageal, gastric, or colorectal cancers was compared with that of 88 healthy controls. Paired t-tests examined cognitive change over time in the cancer group. Hierarchical linear regression examined variables potentially associated with cognitive impairment at 18 months. RESULTS: Forty-eight percent of cancer patients exhibited cognitive impairment 6 months post-cancer diagnosis, and 40% at 18 months. Cancer survivors were impaired relative to controls on measures of sustained attention, memory, and verbal fluency at 18 months, controlling for age. Older age, low hemoglobin, and cancer-related PTSD were associated with worse cognition at 18 months. CONCLUSIONS: CRCI is more frequent in older adults than reported in studies of younger adults and may be more frequent in men. Potential areas of intervention for CRCI include psychotherapy for cancer-related PTSD, treatment of anemia, and awareness of particularly vulnerable cognitive domains such as sustained attention, memory, and verbal fluency.

Impact of Patient-Centered Medical Home Implementation on Diabetes Control in the Veterans Health Administration.

BACKGROUND: Given its widespread dissemination across primary care, the Veterans Health Administration (VA) is an ideal setting to examine the impact of the patient-centered medical home (PCMH) on diabetes outcomes. OBJECTIVE: To assess the impact of PCMH implementation on diabetes outcomes among patients receiving care in the Veterans Health Administration. DESIGN: Retrospective cohort analysis and multilevel logistic regression. PATIENTS: Twenty thousand eight hundred fifty-eight patients in one Midwest VA network who had a diabetes diagnosis in both 2009 and 2012 and who received primary care between October 1, 2008 and September 30, 2009. MAIN MEASURES: Glycemic and lipid control using VA quality indicators [hemoglobin (Hb) A1c < 9%, low-density lipoprotein cholesterol (LDL-C) < 100 mg/dL]. KEY RESULTS: Odds of glycemic control were lower in 2012 than 2009 (OR = 0.72, 95% CI = 0.67-0.77, p < 0.001), and this change in control over time varied by race (OR of the interaction between time and race = 1.18, 95% CI = 1.02-1.36, p = 0.028). While the disparity in glycemic control between white and black patients persisted post-PCMH, the magnitude of the disparity was smaller in 2012 compared to 2009 (2012: OR = 1.32, 95% CI = 1.18-1.47, p < 0.0001 and 2009: OR = 1.59, 95% CI = 1.39-1.82, p < 0.0001). Odds of lipid control did not significantly change between 2009 and 2012 and change did not vary by race and/or gender. CONCLUSIONS: Although there were no significant improvements in odds of lipid control, and odds of glycemic control decreased following PCMH implementation, there was evidence of reduced racial disparities in glycemic control post-PCMH implementation.

Variation in postacute care utilization after complex surgery.

BACKGROUND: Variation in use of postacute care (PAC), including skilled nursing facilities and inpatient rehabilitation, accounts for 73% of regional variation in Medicare spending. Studies of hospital variation in PAC use have typically focused on nonsurgical patients or have been limited to Medicare data. Consequently, there is no nationally representative data on how rates of postoperative discharge to PAC differ between hospitals. The purpose of this study was to explore hospital-level variation in PAC utilization after cardiovascular and abdominal surgery. MATERIALS AND METHODS: We evaluated 3,487,365 patients from the Nationwide Inpatient Sample and 60,666 from the Veterans Affairs health system, who had colorectal surgery, hepatectomy, pancreatectomy, coronary bypass, aortic aneurysm repair, and peripheral vascular bypass from 2008 to 2011. For each hospital, we calculated unadjusted and risk-adjusted observed-to-expected ratios for discharge to PAC facilities (skilled nursing or inpatient rehabilitation). RESULTS: A total of 631,199 (18%) non-veterans and 4744 (8%) veterans were discharged to PAC facilities. For veterans, 32% were ≥70 y old, and 98% were men. For non-veterans, 39% were ≥70, and 60% were men. Hospital rates of discharge to PAC facilities varied from 1% to 36% for veterans hospitals and from 1% to 59% for non-veteran hospitals. Risk-adjusted observed-to-expected ratios ranged from 0.10 to 4.15 for veterans and from 0.11 to 4.3 for non-veteran hospitals. CONCLUSIONS: There is substantial variation in PAC utilization and rates of home discharge after abdominal and cardiovascular surgery. To reduce variation, further research is needed to understand health systems factors that influence PAC utilization.