Crane, Sarah J.

Key points Persons with severe and persistent mental illness (SPMI) experience disparities in accessing and initiating cancer treatment The assertive community treatment (ACT) model promotes the integration of physical and mental health care and improves the quality of life for a patient diagnosed with cancer The ACT model is essential in promoting decisional capacity of those with SPMI and cancer diagnoses The ability to access 24‐h support from the ACT model provides psychological support to patients dealing with chronic physical health diagnoses Collaboration between cancer treatment teams and the multidisciplinary ACT team can improve treatment engagement and enhance a patient's natural support system

The COVID-19 pandemic has disproportionately affected underserved and minority populations in the United States. This is partially attributable to limited access to diagnostic testing from deeply rooted structural inequities precipitating higher infection and mortality rates. We describe the process of establishing a drive-through collection site by leveraging an academic–community partnership between a medical institution and a federally qualified health center in Minnesota. Over 10 weeks, 2006 COVID-19 tests were provided to a socioeconomically disadvantaged population of racial/ethnic minorities and low-income essential workers.

Objective: Much has been written about the patients’ perspective concerning weight management in health care. The purpose of this survey study was to assess perspectives of primary care providers (PCPs) and nurses toward patient weight management and identify possible areas of growth. Patients and Methods: We emailed a weight management–focused survey to 674 eligible participants (437 [64.8%] nurses and 237 [35.2%] PCPs) located in 5 outpatient primary care clinics. The survey focused on opportunities, practices, knowledge, confidence, attitudes, and beliefs. A total of 219 surveys were returned (137 [62.6%] from nurses and 82 [34.4%] from PCPs). Results: Among 219 responders, 85.8% were female and 93.6% were white non-Hispanic. In this study, PCPs and nurses believed obesity to be a major health problem. While PCPs felt more equipped than nurses to address weight management ( P < .001) and reported receiving more training than nurses (50.0% vs 17.6%, respectively), both felt the need for more training on obesity (73.8% and 79.4%, respectively). Although, PCPs also spent more patient contact time providing weight management services versus nurses ( P < .001), the opportunity/practices score was lower for PCPs than nurses (−0.35 ± 0.44 vs −0.17 ± 0.41, P < .001) with PCPs more likely to say they lacked the time to discuss weight and they worried it would cause a poor patient-PCP relationship. The knowledge/confidence score also differed significantly between the groups, with nurses feeling less equipped to deal with weight management issues than PCPs (−0.42 ± 0.43 vs −0.03 ± 0.55, P < .001). Neither group seemed very confident, with those in the PCP group only answering with an average score of neutral. Conclusion: By asking nurses and PCP general questions about experiences, attitudes, knowledge, and opinions concerning weight management in clinical care, this survey has identified areas for growth in obesity management. Both PCPs and nurses would benefit from additional educational training on weight management.

Objectives: Opioid prescribing in the United States has tripled since 1999. At the same time, there has been increasing attention to patient satisfaction. It has been suggested that providers concerned about patient satisfaction may be more likely to treat pain with opioids. We examined primary care providers’ opioid prescribing practices to determine if higher provider opioid prescribing was associated with higher patient satisfaction. Methods: For 77 primary care providers, we compared each provider’s opioid prescription count and amount prescribed to each provider’s patient panel satisfaction measures. Satisfaction measures were obtained from surveys following office visits and consisted of Likert-type scale answers concerning satisfaction for pain management and other provider satisfaction domains. Satisfaction surveys were generated independent of patient complaint of pain and had the aim of overall assessment of patient satisfaction with the provider and the healthcare system. We assessed the correlation between opioid prescribing and patient panel pain management satisfaction using linear regression models with and without adjustment for patient complexity. Results: We observed no statistically significant correlation between patient panel satisfaction with their provider and the quantity of opioids that the provider prescribed (R 2 = 0.006; p = 0.52). There was also no correlation between patient panel satisfaction and the number of opioid prescriptions written by their provider (R 2 = 0.005; p = 0.54). Additional multivariate analysis after adjusting for patient complexity also demonstrated no correlation of pain management satisfaction with opioids prescribed. Although the quantity of opioid prescriptions was not correlated with pain management satisfaction, several other patient satisfaction measures correlated significantly with pain management satisfaction. Conclusion: Primary care providers with a greater rate of opioid prescribing did not have higher patient panel satisfaction scores for pain management. In primary care, providers who want to improve patient satisfaction should focus on other components of patient care besides opioid-based pain management.

Objective: Opioids are being prescribed at increasing rates in primary care practices, and among individual providers there is significant variability in opioid prescribing. Primary care practices also vary significantly in complexity of their patients, ranging from healthy patients to those with multiple comorbidities. Our objective was to examine individual primary care providers for an association between their opioid prescribing and the complexity/risk of their panel of patients (a panel of patients is a group of patients whose medical care is the responsibility of a specific healthcare provider or care team). Methods: We retrospectively examined 12 months of opioid prescription data from a primary care practice. We obtained counts of opioids prescribed by providers in the Mayo Clinic, Rochester, Minnesota primary care practice. For patients paneled (assigned) to family medicine and internal medicine, we used the Centers for Medicare and Medicaid Services hierarchical condition category patient risk score as a measure of patient complexity. After adjusting the opioid counts for panel patient count (to get opioid counts per patient), we used linear regression analysis to determine the correlation between the hierarchical condition category risk and the amount of opioid prescribed by individual providers. Results: Among our combined 103 primary care providers, opioid unit counts prescribed per patient were highly correlated with the providers’ hierarchical condition category panel risk score (r 2 = 0.54). After excluding three outliers, r 2 was 0.74. With and without the outliers, the correlation was very significant (p < 0.0001). Subgroup analysis of panels with hierarchical condition category ⩽ 0.45 showed no correlation of opioid prescribing volume with hierarchical condition category (r 2 < 0.02; p = 0.32). Provider panels with hierarchical condition category > 0.45 showed significant correlation with hierarchical condition category (r 2 = 0.26; p = 0.001). Conclusion: When examining differences in primary care providers’ opioid prescribing practices, the Centers for Medicare and Medicaid Services endorsed risk score (the hierarchical condition category score) can help adjust for population differences of a provider’s patients.

AbstractRationale, aims, and objectivesNovel health care delivery models are needed to reduce health care use while delivering effective and safe care. We developed a model of a neurologist integrated and colocated in primary care leveraging “curbside,” electronic, and traditional consultations. Our objective was to examine the impact on health care resource use of diagnostic testing and referrals for face‐to‐face neurological consultation and adverse outcomes associated with electronic and curbside consultations.MethodsConsecutive patients from December 1, 2014, to March 13, 2015, were included in the analysis about whom contact was made between a primary care clinician and a colocated neurologist.ResultsOver 3.5 months of the pilot, 359 unique patients generated 429 consultations (179 curbsides, 68 electronic consultations, and 182 face‐to‐face visits). The integrated model resulted in avoidance of 78 face‐to‐face tertiary neurology consultations, 39 brain magnetic resonance imaging, 50 electromyograms, and 53 other advanced imaging studies. Earlier curbside consultation may have prevented unnecessary testing or face‐to‐face tertiary neurology consultations in 40 (22%) patients. Earlier face‐to‐face consultation may have avoided expensive testing in 31 (17%) patients. No cases met criteria for an adverse outcome. The number of referrals to tertiary neurology declined by 64%, and the total number of face‐to‐face visits per month declined by 25%.ConclusionColocated neurology in a primary care medical home offers a promising intervention to deliver high‐value care.

ObjectivesPerformance measurement is used by health care providers, payers, and patients. Historically accomplished using administrative data, registries are used increasingly to track and improve care. We assess how measured diabetes care quality differs when calculated using claims versus registry.Data Sources/Study SettingCross‐sectional analysis of administrative claims and electronic health records (EHRs) of patients in a multispecialty integrated health system in 2012 (n = 368,883).Study DesignWe calculated percent of patients attaining glycohemoglobin <8.0 percent, LDL cholesterol <100 mg/dL, blood pressure <140/90 mmHg, and nonsmoking (D4) in cohorts, identified by Medicare Accountable Care Organization/Minnesota Community Measures (ACO‐MNCM; claims‐based), Healthcare Effectiveness Data and Information Set (HEDIS; claims‐based), and registry (EHR‐based).Data Collection/Extraction MethodsClaims were linked to EHR to create a dataset of performance‐eligible patients.Principal FindingsACO‐MNCM, HEDIS, and registry identified 6,475, 6,989, and 6,425 measurement‐eligible patients. Half were common among the methods; discrepancies were due to attribution, age restriction, and encounter requirements. D4 attainment was lower in ACO‐MNCM (36.09 percent) and HEDIS (37.51 percent) compared to registry (43.74 percent) cohorts.ConclusionsRegistry‐ and claims‐based performance measurement methods identify different patients, resulting in different rates of quality metric attainment with implications for innovative population health management.

Introduction: Secure messages and electronic visits (“e-visits”) through patient portals provide patients with alternatives to face-to-face appointments, telephone contact, letters, and e-mails. Limited information exists on how portal messaging impacts face-to-face visits in primary care. Materials and Methods: We conducted a retrospective cohort study of 2,357 primary care patients who used electronic messaging (both secure messages and e-visits) on a patient portal. Face-to-face appointment frequencies (visits/year) of each patient were calculated before and after the first message in a matched-pairs analysis. We analyzed visit frequencies with and without adjustments for a first message surge in visits, and we examined subgroups of high message utilizers and long-term users. Results: Primary care patients who sent at least one message (secure message or e-visit) had a mean of 2.43 (standard deviation [SD] 2.3) annual face-to-face visits before the first message and 2.47 (SD 2.8) after, a nonsignificant difference ( p =0.45). After adjustment for a first message surge in visits, no significant visit frequency differences were observed (mean, 2.35 annual visits per patient both before and after first message; p =0.93). Subgroup analysis also showed no significant change in visit frequency for patients with higher message utilization or for those who had used the messaging feature longer. Conclusions: No significant change in face-to-face visit frequency was observed following implementation of portal messaging. Secure messaging and e-visits through a patient portal may not result in a change of adult primary care face-to-face visits.

Telemedicine practitioners are familiar with multiple barriers to delivering care at a distance. Licensing and reimbursement barriers are well known and are being addressed at national and state levels by the American Telemedicine Association. Another telemedicine barrier comes in the form of quality measures for diabetes. Minnesota medical practices are currently being compared on the proportion of their patients with diabetes who have attained goals for blood pressure, low-density lipoprotein cholesterol, and hemoglobin A1C. The quality measure for blood pressure specifically excludes measurements taken by the patient, thus precluding blood pressure telemonitoring as a way to meet the blood pressure goal. To counter this barrier, advocacy in telemedicine is needed so that telemonitoring as a data collection tool is included in quality measures.