Health Equity in the Veterans Health Administration From Veterans' Perspectives by Race and Sex.

Importance: Advancing equitable patient-centered care in the Veterans Health Administration (VHA) requires understanding the differential experiences of unique patient groups. Objective: To inform a comprehensive strategy for improving VHA health equity through the comparative qualitative analysis of care experiences at the VHA among veterans of Black and White race and male and female sex. Design, Setting, and Participants: This qualitative study used a technique termed freelisting, an anthropologic technique eliciting responses in list form, at an urban academic VHA medical center from August 2, 2021, to February 9, 2022. Participants included veterans with chronic hypertension. The length of individual lists, item order in those lists, and item frequency across lists were used to calculate a salience score for each item, allowing comparison of salient words and topics within and across different groups. Participants were asked about current perceptions of VHA care, challenges in the past year, virtual care, suggestions for change, and experiences of racism. Data were analyzed from February 10 through September 30, 2022. Main Outcomes and Measures: The Smith salience index, which measures the frequency and rank of each word or phrase, was calculated for each group. Results: Responses from 49 veterans (12 Black men, 12 Black women, 12 White men, and 13 White women) were compared by race (24 Black and 25 White) and sex (24 men and 25 women). The mean (SD) age was 64.5 (9.2) years. Some positive items were salient across race and sex, including "good medical care" and telehealth as a "comfortable/great option," as were some negative items, including "long waits/delays in getting care," "transportation/traffic challenges," and "anxiety/stress/fear." Reporting "no impact" of racism on experiences of VHA health care was salient across race and sex; however, reports of race-related unprofessional treatment and active avoidance of race-related conflict differed by race (present among Black and not White participants). Experiences of interpersonal interactions also diverged. "Impersonal/cursory" telehealth experiences and the need for "more personal/attentive" care were salient among women and Black participants, but not men or White participants, who associated VHA care with courtesy and respect. Conclusions and Relevance: In this qualitative freelist study of veteran experiences, divergent experiences of interpersonal care by race and sex provided insights for improving equitable, patient-centered VHA care. Future research and interventions could focus on identifying differences across broader categories both within and beyond race and sex and bolstering efforts to improve respect and personalized care to diverse veteran populations.

Identifying Medication-Related Intents From a Bidirectional Text Messaging Platform for Hypertension Management Using an Unsupervised Learning Approach: Retrospective Observational Pilot Study.

BACKGROUND: Free-text communication between patients and providers plays an increasing role in chronic disease management, through platforms varying from traditional health care portals to novel mobile messaging apps. These text data are rich resources for clinical purposes, but their sheer volume render them difficult to manage. Even automated approaches, such as natural language processing, require labor-intensive manual classification for developing training data sets. Automated approaches to organizing free-text data are necessary to facilitate use of free-text communication for clinical care. OBJECTIVE: The aim of this study was to apply unsupervised learning approaches to (1) understand the types of topics discussed and (2) learn medication-related intents from messages sent between patients and providers through a bidirectional text messaging system for managing participant blood pressure (BP). METHODS: This study was a secondary analysis of deidentified messages from a remote, mobile, text-based employee hypertension management program at an academic institution. We trained a latent Dirichlet allocation (LDA) model for each message type (ie, inbound patient messages and outbound provider messages) and identified the distribution of major topics and significant topics (probability >.20) across message types. Next, we annotated all medication-related messages with a single medication intent. Then, we trained a second medication-specific LDA (medLDA) model to assess how well the unsupervised method could identify more fine-grained medication intents. We encoded each medication message with n-grams (n=1-3 words) using spaCy, clinical named entities using Stanza, and medication categories using MedEx; we then applied chi-square feature selection to learn the most informative features associated with each medication intent. RESULTS: In total, 253 participants and 5 providers engaged in the program, generating 12,131 total messages: 46.90% (n=5689) patient messages and 53.10% (n=6442) provider messages. Most patient messages corresponded to BP reporting, BP encouragement, and appointment scheduling; most provider messages corresponded to BP reporting, medication adherence, and confirmatory statements. Most patient and provider messages contained 1 topic and few contained more than 3 topics identified using LDA. In total, 534 medication messages were annotated with a single medication intent. Of these, 282 (52.8%) were patient medication messages: most referred to the medication request intent (n=134, 47.5%). Most of the 252 (47.2%) provider medication messages referred to the medication question intent (n=173, 68.7%). Although the medLDA model could identify a majority intent within each topic, it could not distinguish medication intents with low prevalence within patient or provider messages. Richer feature engineering identified informative lexical-semantic patterns associated with each medication intent class. CONCLUSIONS: LDA can be an effective method for generating subgroups of messages with similar term usage and facilitating the review of topics to inform annotations. However, few training cases and shared vocabulary between intents precludes the use of LDA for fully automated, deep, medication intent classification. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1101/2021.12.23.21268061.

Clinician Decisions After Notification of Elevated Blood Pressure Measurements From Patients in a Remote Monitoring Program.

Importance: Guidelines recommend using telehealth for hypertension management, but insufficient evidence is available to guide strategies for incorporating telehealth data into clinical practice. Objective: To describe how primary care teams responded to elevated remote blood pressure (BP) alerts in the electronic health record (EHR) in a randomized clinical trial of BP telemonitoring conducted in routine practice settings. Design, Setting, and Participants: This retrospective cohort study reviewed EHR documentation from May 8, 2018, to August 9, 2019, in a single urban academic family practice site. Primary care teams comprising 28 attending physicians and nurse practitioners, residents, and nurses cared for 162 patients in a text-based clinical trial of remote BP monitoring remote BP monitoring. Data were analyzed from October 21, 2019, to April 30, 2021. Exposures: Clinicians received a direct message in their EHR inbox when patients submitted at least 3 elevated BP readings. Main Outcomes and Measures: Categories and frequencies of clinician action, created via review of EHR-documented clinician responses to EHR alerts by 2 physicians. Results: Patients in this study (n = 162) were predominantly female (111 [68.5%]) and Black or African American (146 [90.1%]), whereas attending physicians (n = 21) were predominantly female (13 [61.9%]) and non-Hispanic White (19 [90.5%]) with a mean (SD) age of 51.6 (11.1) years. Five hundred fifty-two alerts fell into 12 categories of clinical actions. Clinicians acted on 343 alerts (62.1%). Common remote activities were to reconcile medications and assess adherence (120 of 552 alerts [21.7%]) and verify BP measurement technique (65 of 552 alerts [11.8%]). Clinicians also commonly requested appointments (120 of 552 alerts [21.7%]) and/or saw the patient in a subsequent office visit (114 of 552 alerts [20.7%]). Ninety-six alerts (17.4%) resulted in medication changes; half of these changes were remote (48 of 96 [50.0%]), and the other half were visit-based. For 209 of 552 alerts (37.9%), no changes were made to the care plan, typically without documenting clinical rationale (196 of 209 instances [93.8%]). Exploratory EHR review was used to infer potential clinical rationale for 106 (54.1%) of such cases, but there was insufficient information for the remaining 90 (45.9%). Conclusions and Relevance: These findings suggest that EHR alerts for elevated BP during remote monitoring were effective in prompting a mix of remote and office-based management. It was also common for the plan of care to remain unchanged, possibly suggesting need for more refined alerts and improved clinician support.

Patient Interaction Phenotypes With an Automated Remote Hypertension Monitoring Program and Their Association With Blood Pressure Control: Observational Study.

BACKGROUND: Automated texting platforms have emerged as a tool to facilitate communication between patients and health care providers with variable effects on achieving target blood pressure (BP). Understanding differences in the way patients interact with these communication platforms can inform their use and design for hypertension management. OBJECTIVE: Our primary aim was to explore the unique phenotypes of patient interactions with an automated text messaging platform for BP monitoring. Our secondary aim was to estimate associations between interaction phenotypes and BP control. METHODS: This study was a secondary analysis of data from a randomized controlled trial for adults with poorly controlled hypertension. A total of 201 patients with established primary care were assigned to the automated texting platform; messages exchanged throughout the 4-month program were analyzed. We used the k-means clustering algorithm to characterize two different interaction phenotypes: program conformity and engagement style. First, we identified unique clusters signifying differences in program conformity based on the frequency over time of error alerts, which were generated to patients when they deviated from the requested text message format (eg, ###/## for BP). Second, we explored overall engagement styles, defined by error alerts and responsiveness to text prompts, unprompted messages, and word count averages. Finally, we applied the chi-square test to identify associations between each interaction phenotype and achieving the target BP. RESULTS: We observed 3 categories of program conformity based on their frequency of error alerts: those who immediately and consistently submitted texts without system errors (perfect users, 51/201), those who did so after an initial learning period (adaptive users, 66/201), and those who consistently submitted messages generating errors to the platform (nonadaptive users, 38/201). Next, we observed 3 categories of engagement style: the enthusiast, who tended to submit unprompted messages with high word counts (17/155); the student, who inconsistently engaged (35/155); and the minimalist, who engaged only when prompted (103/155). Of all 6 phenotypes, we observed a statistically significant association between patients demonstrating the minimalist communication style (high adherence, few unprompted messages, limited information sharing) and achieving target BP (P<.001). CONCLUSIONS: We identified unique interaction phenotypes among patients engaging with an automated text message platform for remote BP monitoring. Only the minimalist communication style was associated with achieving target BP. Identifying and understanding interaction phenotypes may be useful for tailoring future automated texting interactions and designing future interventions to achieve better BP control.

High-Cost Patients: Hot-Spotters Don't Explain the Half of It.

BACKGROUND: Understanding resource utilization patterns among high-cost patients may inform cost reduction strategies. OBJECTIVE: To identify patterns of high-cost healthcare utilization and associated clinical diagnoses and to quantify the significance of hot-spotters among high-cost users. DESIGN: Retrospective analysis of high-cost patients in 2012 using data from electronic medical records, internal cost accounting, and the Centers for Medicare and Medicaid Services. K-medoids cluster analysis was performed on utilization measures of the highest-cost decile of patients. Clusters were compared using clinical diagnoses. We defined "hot-spotters" as those in the highest-cost decile with ≥4 hospitalizations or ED visits during the study period. PARTICIPANTS AND EXPOSURE: A total of 14,855 Medicare Fee-for-service beneficiaries identified by the Medicare Quality Resource and Use Report as having received 100 % of inpatient care and ≥90 % of primary care services at Cleveland Clinic Health System (CCHS) in Northeast Ohio. The highest-cost decile was selected from this population. MAIN MEASURES: Healthcare utilization and diagnoses. KEY RESULTS: The highest-cost decile of patients (n = 1486) accounted for 60 % of total costs. We identified five patient clusters: "Ambulatory," with 0 admissions; "Surgical," with a median of 2 surgeries; "Critically Ill," with a median of 4 ICU days; "Frequent Care," with a median of 2 admissions, 3 ED visits, and 29 outpatient visits; and "Mixed Utilization," with 1 median admission and 1 ED visit. Cancer diagnoses were prevalent in the Ambulatory group, care complications in the Surgical group, cardiac diseases in the Critically Ill group, and psychiatric disorders in the Frequent Care group. Most hot-spotters (55 %) were in the "frequent care" cluster. Overall, hot-spotters represented 9 % of the high-cost population and accounted for 19 % of their overall costs. CONCLUSIONS: High-cost patients are heterogeneous; most are not so-called "hot-spotters" with frequent admissions. Effective interventions to reduce costs will require a more multi-faceted approach to the high-cost population.