Impact of the Registered Nurse Clinical Liaison Role in Ambulatory Care on Transitions of Care: A Retrospective Cohort Study.

PURPOSE OF STUDY: To determine the relationship between engagement with the novel register nurse care liaison (RNCL) and enrollment in care management compared with usual care in hospitalized patients. PRIMARY PRACTICE SETTING: Patients in the hospital from January 1, 2019, to September 30, 2019, who would be eligible for care management. METHODOLOGY AND SAMPLE: This was a retrospective cohort study. The authors compared a group of 419 patients who utilized the services of the RNCL at any time during their hospital stay with the RNCL to a propensity matched control group of 833 patients, which consisted of patients who were hospitalized during the same time as the RNCL intervention group. Our primary outcome was enrollment in care management programs. Our secondary outcome was 30-day readmissions, emergency department (ED) use, and office visits. The authors compared baseline characteristics and outcomes across groups using Wilcoxon-Mann-Whitney and χ2 tests and performed an adjusted analysis using conditional logistic regression models controlling for patient education and previous health care utilization. RESULTS: The authors matched 419 patients who had engaged an RNCL to 833 patients in the usual care group; this comprised the analytic cohort for this study. The authors found 67.1% of patients enrolled in a care management program with RNCL compared with only 15.3% in usual care (p < .0001). The authors found higher rates of enrollment in all programs of care management. After the full adjustment, the odds ratio for enrollment in any program was 13.7 (95% confidence interval: 9.3, 20.2) for RNCL compared with usual care. There was no difference between groups with 30-day hospitalization or ED visit. CONCLUSION: In this matched study of 419 patients with RNCL engagement, the authors found significantly higher enrollment in all care management programs. IMPLICATIONS FOR CASE MANAGEMENT PRACTICE: These findings encourage further study of this care model. This could help enhance enrollment in care management programs, increase relationships between inpatient practice and ambulatory practice, as well as increase communication across the continuum of care.

Hepatitis B Screening of At-Risk Immigrants Seen at Primary Care Clinics: A Quality Improvement Project.

OBJECTIVE: To test an intervention to increase screening for hepatitis B (HBV) in at-risk immigrants in the primary care setting. PATIENTS AND METHODS: From a Mayo Clinic primary care panel, we identified approximately 19,000 immigrant patients from 9 high-risk countries/ethnic groups with intermediate or high prevalences of chronic HBV. Eligible patients with no record of prior HBV testing scheduled for primary care visits within the study period spanning October 1, 2017, through October 31, 2018, were identified. During the intervention period, the primary health care professional was notified by email 1 week prior to each primary care visit and encouraged to discuss screening for HBV infection and order screening tests at the appointment. We assessed rates of HBV screening during control and intervention periods. RESULTS: We identified 597 patients in the control period and 212 patients in the intervention period who had not been screened previously for HBV. During the intervention period, 31.4% (58) of the 185 eligible patients were screened for HBV vs 7.2% (43) of the 597 eligible patients in the control period. Thus, the intervention resulted in a 4.3-fold increase in screening (P<.00001). Of the 101 patients screened in the at-risk population, 22 (21.8%) screened positive for prior exposure to HBV (hepatitis B core antibody-positive) and 6 (5.9%) for chronic HBV infection (hepatitis B surface antigen-positive). CONCLUSION: Notifying primary care physicians of the high-risk status of immigrant patients substantially increased screening for HBV. Identifying patients with HBV is important for monitoring disease prevalence, preventing transmission, and initiating treatment and cancer surveillance, allowing earlier recognition and prevention of chronic hepatitis, disease reactivation, cirrhosis, and hepatocellular carcinoma.

Outcomes of COVID-19 With the Mayo Clinic Model of Care and Research.

OBJECTIVE: To report the Mayo Clinic experience with coronavirus disease 2019 (COVID-19) related to patient outcomes. METHODS: We conducted a retrospective chart review of patients with COVID-19 diagnosed between March 1, 2020, and July 31, 2020, at any of the Mayo Clinic sites. We abstracted pertinent comorbid conditions such as age, sex, body mass index, Charlson Comorbidity Index variables, and treatments received. Factors associated with hospitalization and mortality were assessed in univariate and multivariate models. RESULTS: A total of 7891 patients with confirmed COVID-19 infection with research authorization on file received care across the Mayo Clinic sites during the study period. Of these, 7217 patients were adults 18 years or older who were analyzed further. A total of 897 (11.4%) patients required hospitalization, and 354 (4.9%) received care in the intensive care unit (ICU). All hospitalized patients were reviewed by a COVID-19 Treatment Review Panel, and 77.5% (695 of 897) of inpatients received a COVID-19-directed therapy. Overall mortality was 1.2% (94 of 7891), with 7.1% (64 of 897) mortality in hospitalized patients and 11.3% (40 of 354) in patients requiring ICU care. CONCLUSION: Mayo Clinic outcomes of patients with COVID-19 infection in the ICU, hospital, and community compare favorably with those reported nationally. This likely reflects the impact of interprofessional multidisciplinary team evaluation, effective leveraging of clinical trials and available treatments, deployment of remote monitoring tools, and maintenance of adequate operating capacity to not require surge adjustments. These best practices can help guide other health care systems with the continuing response to the COVID-19 pandemic.

Drive-Through Testing: A Unique, Efficient Method of Collecting Large Volume of Specimens During the SARS-CoV-2 (COVID-19) Pandemic.

The World Health Organization declared COVID-19 a global pandemic in March 2020. A major challenge in this worldwide pandemic has been efficient and effective large-scale testing for the disease. In this communication, we discuss lessons learned in the set up and function of a locally organized drive-through testing facility.

Identifying Opportunities for Advancing Weight Management in Primary Care.

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.

Do primary care providers who prescribe more opioids have higher patient panel satisfaction scores?

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 (R2 = 0.006; p = 0.52). There was also no correlation between patient panel satisfaction and the number of opioid prescriptions written by their provider (R2 = 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.

Effect of integrated community neurology on utilization, diagnostic testing, and access.

BACKGROUND: The primary care medical home (PCMH) aims to promote delivery of high-value health care. However, growing demand for specialists due to increasingly older adults with complicated and chronic disease necessitates development of novel care models that efficiently incorporate specialty expertise while maintaining coordination and continuity with the PCMH. We describe the effect of a model of integrated community neurology (ICN) on health care utilization, diagnostic testing, and access. METHODS: This is a retrospective, matched case-control comparison of patients referred to ICN for a face-to-face consultation over a 12-month period. The control group consisted of propensity score-matched patients referred to a non-colocated neurology practice during the study period. Administrative data were used to assess for diagnostic testing, visit utilization, and patient time to appointment. RESULTS: From October 1, 2014, to September 30, 2015, we identified 459 patients evaluated by ICN for a face-to-face visit and 459 matched controls evaluated by the non-colocated neurology practice. The majority of patients were Caucasian and female. ICN patients had lower odds of EMGs ordered (adjusted odds ratio [OR] 0.64; 95% confidence interval [CI] 0.46-0.89; p = 0.009), MRI brain (adjusted OR 0.60; 95% CI 0.45-0.79; p = 0.0004), or subsequent referral to outpatient neurology (adjusted OR 0.62; 95% CI 0.47-0.83; p = 0.001). ICN was not associated with an increase in emergency department visits, hospitalizations, or appointment wait time. CONCLUSIONS: The ICN model in a PCMH has the potential to reduce diagnostic testing and utilization.

Association of provider opioid prescribing practices and the Centers for Medicare and Medicaid Services hierarchical condition category score: A retrospective examination of correlation between the volume of provider-prescribed opioid medications and provider panel complexity.

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 (r2 = 0.54). After excluding three outliers, r2 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 (r2 < 0.02; p = 0.32). Provider panels with hierarchical condition category > 0.45 showed significant correlation with hierarchical condition category (r2 = 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.

Pilot of integrated, colocated neurology in a primary care medical home.

RATIONALE, AIMS, AND OBJECTIVES: Novel 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. METHODS: Consecutive 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. RESULTS: Over 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%. CONCLUSION: Colocated neurology in a primary care medical home offers a promising intervention to deliver high-value care.

The Method for Performance Measurement Matters: Diabetes Care Quality as Measured by Administrative Claims and Institutional Registry.

OBJECTIVES: Performance 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 SETTING: Cross-sectional analysis of administrative claims and electronic health records (EHRs) of patients in a multispecialty integrated health system in 2012 (n = 368,883). STUDY DESIGN: We 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 METHODS: Claims were linked to EHR to create a dataset of performance-eligible patients. PRINCIPAL FINDINGS: ACO-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. CONCLUSIONS: Registry- and claims-based performance measurement methods identify different patients, resulting in different rates of quality metric attainment with implications for innovative population health management.

Patient-reported geriatric symptoms as risk factors for hospitalization and emergency department visits.

There is an urgent need to identify predictors of adverse outcomes and increased health care utilization in the elderly. The Mayo Ambulatory Geriatric Evaluation (MAGE) is a symptom questionnaire that was completed by patients aged 65 years and older during office visits to Primary Care Internal Medicine at Mayo Clinic in Rochester, MN. It was introduced to improve screening for geriatric conditions. We conducted this study to explore the relationship between self-reported geriatric symptoms and hospitalization and emergency department (ED) visits within 1 year of completing the survey. This was a retrospective cohort study of patients who completed the MAGE from April 2008 to December 2010. The primary outcome was an ED visit or hospitalization within 1 year. Predictors included responses to individual questions in the MAGE. Data were obtained from the electronic medical record and administrative records. Logistic regression analyses were performed from significant univariate factors to determine predictors in a multivariable setting. A weighted scoring system was created based upon the odds ratios derived from a bootstrap process. The sensitivity, specificity, and AUC were calculated using this scoring system. The MAGE survey was completed by 7738 patients. The average age was 76.2 ± 7.68 years and 57% were women. Advanced age, a self-report of worse health, history of 2 or more falls, weight loss, and depressed mood were significantly associated with hospitalization or ED visits within 1 year. A score equal to or greater than 2 had a sensitivity of 0.74 and specificity of 0.45. The calculated AUC was 0.60. The MAGE questionnaire, which was completed by patients at an outpatient visit to screen for common geriatric issues, could also be used to assess risk for ED visits and hospitalization within 1 year.

Telemedicine barriers associated with regional quality measures.

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.

Impact of patient portal secure messages and electronic visits on adult primary care office visits.

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.

Patient-generated secure messages and eVisits on a patient portal: are patients at risk?

BACKGROUND: Patient portals are becoming increasingly common, but the safety of patient messages and eVisits has not been well studied. Unlike patient-to-nurse telephonic communication, patient messages and eVisits involve an asynchronous process that could be hazardous if patients were using it for time-sensitive symptoms such as chest pain or dyspnea. METHODS: We retrospectively analyzed 7322 messages (6430 secure messages and 892 eVisits). To assess the overall risk associated with the messages, we looked for deaths within 30 days of the message and hospitalizations and emergency department (ED) visits within 7 days following the message. We also examined message content for symptoms of chest pain, breathing concerns, and other symptoms associated with high risk. RESULTS: Two deaths occurred within 30 days of a patient-generated message, but were not related to the message. There were six hospitalizations related to a previous secure message (0.09% of secure messages), and two hospitalizations related to a previous eVisit (0.22% of eVisits). High-risk symptoms were present in 3.5% of messages but a subject line search to identify these high-risk messages had a sensitivity of only 15% and a positive predictive value of 29%. CONCLUSIONS: Patients use portal messages 3.5% of the time for potentially high-risk symptoms of chest pain, breathing concerns, abdominal pain, palpitations, lightheadedness, and vomiting. Death, hospitalization, or an ED visit was an infrequent outcome following a secure message or eVisit. Screening the message subject line for high-risk symptoms was not successful in identifying high-risk message content.

Use of the elderly risk assessment (ERA) index to predict 2-year mortality and nursing home placement among community dwelling older adults.

The objective was to determine the relationship between a high score on the ERA index and 2-year mortality and nursing home placement. As of January 1, 2005, 12,650 community-dwelling patients over 60 years of age were impaneled with a primary care practice at the Mayo Clinic in Rochester, MN. This was a retrospective cohort study utilizing an administrative risk score, the ERA score. Primary outcomes were 2-year mortality and 2-year nursing home placement. The predictor variable was ERA score. Relative risk estimates were used to describe the association between the ERA index and mortality and nursing home placement. The relative risk of 2-year mortality was 51.4 (95% confidence interval=CI=28.0-94.4) in patients in the highest risk group compared to the lowest group. The relative risk of nursing home placement was 113.2 (95% CI=76.1-168.4). Patients with high ERA scores are at high risk for 2-year mortality and 2-year nursing home placement. These findings suggest that the utilization of an electronic risk score can help identify patients at risk for death or nursing home placement. Clinically, the identification of high risk individuals may be useful for utilization of clinical case management.

Patient portal doldrums: does an exam room promotional video during an office visit increase patient portal registrations and portal use?

The patient portal is a web service which allows patients to view their electronic health record, communicate online with their care teams, and manage healthcare appointments and medications. Despite advantages of the patient portal, registrations for portal use have often been slow. Using a secure video system on our existing exam room electronic health record displays during regular office visits, the authors showed patients a video which promoted use of the patient portal. The authors compared portal registrations and portal use following the video to providing a paper instruction sheet and to a control (no additional portal promotion). From the 12,050 office appointments examined, portal registrations within 45 days of the appointment were 11.7%, 7.1%, and 2.5% for video, paper instructions, and control respectively (p<0.0001). Within 6 months following the interventions, 3.5% in the video cohort, 1.2% in the paper, and 0.75% of the control patients demonstrated portal use by initiating portal messages to their providers (p<0.0001).

Use of an electronic administrative database to identify older community dwelling adults at high-risk for hospitalization or emergency department visits: the elders risk assessment index.

BACKGROUND: The prevention of recurrent hospitalizations in the frail elderly requires the implementation of high-intensity interventions such as case management. In order to be practically and financially sustainable, these programs require a method of identifying those patients most at risk for hospitalization, and therefore most likely to benefit from an intervention. The goal of this study is to demonstrate the use of an electronic medical record to create an administrative index which is able to risk-stratify this heterogeneous population. METHODS: We conducted a retrospective cohort study at a single tertiary care facility in Rochester, Minnesota. Patients included all 12,650 community-dwelling adults age 60 and older assigned to a primary care internal medicine provider on January 1, 2005. Patient risk factors over the previous two years, including demographic characteristics, comorbid diseases, and hospitalizations, were evaluated for significance in a logistic regression model. The primary outcome was the total number of emergency room visits and hospitalizations in the subsequent two years. Risk factors were assigned a score based on their regression coefficient estimate and a total risk score created. This score was evaluated for sensitivity and specificity. RESULTS: The final model had an AUC of 0.678 for the primary outcome. Patients in the highest 10% of the risk group had a relative risk of 9.5 for either hospitalization or emergency room visits, and a relative risk of 13.3 for hospitalization in the subsequent two year period. CONCLUSIONS: It is possible to create a screening tool which identifies an elderly population at high risk for hospital and emergency room admission using clinical and administrative data readily available within an electronic medical record.