SHARING Choices: Lessons Learned from a Primary-Care Focused Advance Care Planning Intervention.

BACKGROUND: Few advance care planning (ACP) interventions have been scaled in primary care. PROBLEM: Best practices for delivering ACP at scale in primary care do not exist and prior efforts have excluded older adults with Alzheimer's Disease and Related Dementias (ADRD). INTERVENTION: SHARING Choices (NCT#04819191) is a multicomponent cluster-randomized pragmatic trial conducted at 55 primary care practices from two care delivery systems in the Mid-Atlantic region of the U.S. We describe the process of implementing SHARING Choices within 19 practices randomized to the intervention, summarize fidelity to planned implementation, and discuss lessons learned. OUTCOMES: Embedding SHARING Choices involved engagement with organizational and clinic-level partners. Of 23,220 candidate patients, 17,931 outreach attempts by phone (77.9%) and the patient portal (22.1%) were made by ACP facilitators and 1215 conversations occurred. Most conversations (94.8%) were less than 45 minutes duration. Just 13.1% of ACP conversations included family. Patients with ADRD comprised a small proportion of patients who engaged in ACP. Implementation adaptations included transitioning to remote modalities, aligning ACP outreach with the Medicare Annual Wellness Visit, accommodating primary care practice flexibility. LESSONS LEARNED: Study findings reinforce the value of adaptable study design; co-designing workflow adaptations with practice staff; adapting implementation processes to fit the unique needs of two health systems; and modifying efforts to meet health system goals and priorities.

Using Machine Learning to Capture Quality Metrics from Natural Language: A Case Study of Diabetic Eye Exams.

BACKGROUND AND OBJECTIVE: The prevalence of value-based payment models has led to an increased use of the electronic health record to capture quality measures, necessitating additional documentation requirements for providers. METHODS: This case study uses text mining and natural language processing techniques to identify the timely completion of diabetic eye exams (DEEs) from 26,203 unique clinician notes for reporting as an electronic clinical quality measure (eCQM). Logistic regression and support vector machine (SVM) using unbalanced and balanced datasets, using the synthetic minority over-sampling technique (SMOTE) algorithm, were evaluated on precision, recall, sensitivity, and f1-score for classifying records positive for DEE. We then integrate a high precision DEE model to evaluate free-text clinical narratives from our clinical EHR system. RESULTS: Logistic regression and SVM models had comparable f1-score and specificity metrics with models trained and validated with no oversampling favoring precision over recall. SVM with and without oversampling resulted in the best precision, 0.96, and recall, 0.85, respectively. These two SVM models were applied to the unannotated 31,585 text segments representing 24,823 unique records and 13,714 unique patients. The number of records classified as positive for DEE using the SVM models ranged from 667 to 8,935 (2.7-36% out of 24,823, respectively). Unique patients classified as positive for DEE ranged from 3.5 to 41.8% highlighting the potential utility of these models. DISCUSSION: We believe the impact of oversampling on SVM model performance to be caused by the potential of overfitting of the SVM SMOTE model on the synthesized data and the data synthesis process. However, the specificities of SVM with and without SMOTE were comparable, suggesting both models were confident in their negative predictions. By prioritizing to implement the SVM model with higher precision over sensitivity or recall in the categorization of DEEs, we can provide a highly reliable pool of results that can be documented through automation, reducing the burden of secondary review. Although the focus of this work was on completed DEEs, this method could be applied to completing other necessary documentation by extracting information from natural language in clinician notes. CONCLUSION: By enabling the capture of data for eCQMs from documentation generated by usual clinical practice, this work represents a case study in how such techniques can be leveraged to drive quality without increasing clinician work.

Cognitive Dysfunction, Medication Management, and the Risk of Readmission in Hospital Inpatients.

OBJECTIVES: To determine whether cognitive dysfunction, in particular impaired executive function, may be a risk factor for early readmission in older adults independently managing their medications. DESIGN: Prospective observational study. SETTING: Tertiary hospital. PARTICIPANTS: Individuals aged 65 years and older discharged to home from the medicine service of a tertiary hospital (N = 452). MEASUREMENTS: Participants underwent a cognitive assessment including the Short Blessed Test (SBT), the executive function component of the Montreal Cognitive Assessment, and the Trail-Making Test Part B (TMT-B). Hospital use and demographic data were obtained. A logistic regression model was used to fit the likelihood of readmission on the basis of participant characteristics, medication management, and cognitive performance. Likelihood of hospital readmission within 30 days was determined. RESULTS: For participants managing medications themselves, adjusted 30-day odds of readmission increased 13% on average with each point decrease in SBT score (P = .003) and 9% on average with each 0.01 decrease in TMT-B score (P = .02). For participants who were independent in medication management with more than seven medications, the odds of 30-day readmission increased 16% on average with each point decrease in SBT score (P = .01) and 15% on average with each 0.01 decrease in TMT-B score (P = .03). CONCLUSION: Cognitive dysfunction, particularly executive dysfunction, is a risk factor for readmission in individuals managing their own medications. This risk is greater in individuals taking more than seven medications. The interaction of cognitive function, medication management, and number of medications may enhance risk-stratification efforts to identify individuals at risk of early readmission.

Quality of care and racial disparities in medicare among potential ACOs.

BACKGROUND: The Medicare Accountable Care Organization (ACO) programs encourage integration of providers into large groups and reward provider groups for improving quality, but not explicitly for reducing health care disparities. Larger group size and better overall quality may or may not be associated with smaller disparities. OBJECTIVE: To examine differences in patient characteristics between provider groups sufficiently large to participate in ACO programs and smaller groups; the association between group size and racial disparities in quality; and the association between quality and disparities among larger groups. DESIGN AND PARTICIPANTS: Using 2009 Medicare claims for 3.1 million beneficiaries with cardiovascular disease or diabetes and linked data on provider groups, we compared racial differences in quality by provider group size, adjusting for patient characteristics. Among larger groups, we used multilevel models to estimate correlations between group performance on quality measures for white beneficiaries and black-white disparities within groups. MAIN MEASURES: Four process measures of quality, hospitalization for ambulatory care-sensitive conditions (ACSCs) related to cardiovascular disease or diabetes, and hospitalization for any ACSC. KEY RESULTS: Beneficiaries served by larger groups were more likely to be white and live in areas with less poverty and more education. Larger group size was associated with smaller disparities in low-density lipoprotein (LDL) cholesterol testing and retinal exams, but not in other process measures or hospitalization for ACSCs. Among larger groups, better quality for white beneficiaries in one measure (hospitalization for ACSCs related to cardiovascular disease or diabetes) was correlated with smaller racial disparities (r = 0.28; P = 0.02), but quality was not correlated with disparities in other measures. CONCLUSIONS: Larger provider group size and better performance on quality measures were not consistently associated with smaller racial disparities in care for Medicare beneficiaries with cardiovascular disease or diabetes. ACO incentives rewarding better quality for minority groups and payment arrangements supporting ACO development in disadvantaged communities may be required for ACOs to promote greater equity in care.

Minimally invasive methods for bulbar urethral strictures: a survey of members of the American Urological Association.

OBJECTIVE: To determine current practice patterns, we mailed a questionnaire regarding urethral stricture evaluation, treatment, and follow-up to members of the American Urological Association (AUA). The minimally invasive methods used for treating and evaluating anterior urethral strictures vary widely among clinicians. METHODS: A nationwide survey of practicing members of the AUA was performed by mailed questionnaires. Surveys were mailed to 1262 Urologists, randomly selected from all 50 states. Four-hundred thirty-one urologists (34%) completed the questionnaire and formed the basis for our analysis. RESULTS: Most urologists (63%) treat 6-20 urethral strictures per year. The most common minimally invasive procedures used for managing anterior urethral strictures were dilation (92.8%), cold-knife optical internal urethrotomy (85.6%), endourethral stent (23.4%), laser urethrotomy (19%), and periurethral steroid injection after urethrotomy (7.9%). Most urologists will perform urethrotomy on bulbar strictures up to 2 cm (68.7%) and leave a Foley catheter in place for 1 week or less (86.5%). Technical method of urethrotomy is commonly 1 cut at 12 o'clock (86.3%) or radial cuts (12.1%). Recommended follow-up diagnostic tests after urethrotomy included flow rate (62.9%) and, to a lesser degree (with roughly one-third each), cystoscopy, urethral calibration, and the International Prostate Symptom Score (IPSS). Other tests, such as ultrasonography or urethrography were rarely used. CONCLUSION: Our survey provides information regarding current minimally invasive management and follow-up practice strategies recommended by members of the AUA for anterior urethral strictures. Many common practices in the treatment of anterior urethral stricture disease are not supported in the literature.