Citations

Abstract Objective Financial impacts associated with a switch to a different electronic health record (EHR) have been documented. Less attention has been focused on the patient response to an EHR switch. The Mayo Clinic was involved in an EHR switch that occurred at 6 different locations and with 4 different “go-live” dates. We sought to understand the relationship between patient satisfaction and the transition to a new EHR. Materials and Methods We used patient satisfaction data collected by Press Ganey from July 2016 through December 2019. Our patient satisfaction measure was the percent of patients responding “very good” (top box) to survey questions. Twenty-four survey questions were summarized by Press Ganey into 6 patient satisfaction domains. Piecewise linear regression was used to model patient satisfaction before and after the EHR switch dates. Results Significant drops in patient satisfaction were associated with the EHR switch. Patient satisfaction with access (ease of getting clinic on phone, ease of scheduling appointments, etc.) was most affected (range of 6 sites absolute decline: -3.4% to -8.8%; all significant at 99% confidence interval). Satisfaction with providers was least affected (range of 6 sites absolute decline: -0.5% to -2.8%; 4 of 6 sites significant at 99% confidence interval). After 9-15 months, patient satisfaction with access climbed back to pre-EHR switch levels. Conclusions Patient satisfaction in several patient experience domains dropped significantly and stayed lower than pre–“go-live” for several months after a switch in EHR. Satisfaction with providers declined less than satisfaction with access.

Background: Patient satisfaction surveys ask patients specific questions about provider behavior such as whether they were satisfied with the provider’s instructions about medications or time spent with the patient. It’s unclear how responses to these surveys can help providers focus on specific behaviors to improve. Methods: In a primary care setting, we analyzed Press Ganey patient experience survey responses. We examined the 10 questions dealing with satisfaction specific to the care provider experience. We used the “Top Box” counts (counts of most favorable responses) and Top Box% (percentage of most favorable response) for categorical and continuous measures of patient satisfaction. Results: For 12 consecutive months, 652 providers of 1014 accumulated at least 300 total responses from patients for the 10 provider-related questions. Only 8 of the 652 providers had significant differences ( P < .05) in Top Box% for the 10 questions. Correlation of responses between the questions were between 0.86 and 0.96. Analysis of variance showed that 87% of the total variation in the Top Box% of the 10 questions was between providers and only 13% within providers. Factor analysis found no independent factors within the 10 questions (ie, a one factor model was sufficient; P < .0001). Conclusion: Patient survey questions appear to ask about specific provider behaviors that contribute to patient experience. However, the responses to 10 different questions are highly correlated and may not give providers or management enough statistically significant information to focus patient experience improvement efforts for individual providers.

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.

Background: There are numerous recommendations from expert sources that help guide primary care providers in cancer screening, infectious disease screening, metabolic screening, monitoring of drug levels, and chronic disease management. Little is known about the potential effort needed for a healthcare system to address these recommendations, or the patient effort needed to complete the recommendations. Methods: For 73 recommended population healthcare items, we examined each of 28,742 patients in a primary care internal medicine practice to determine whether they were up-to-date on recommended screening, immunizations, counseling, and chronic disease management goals. We used a rule-based software tool that queries the medical record for diagnoses, dates, laboratory values, pathology reports, and other information used in creating the individualized recommendations. We counted the number of uncompleted recommendations by age groups and examined the healthcare staff needed to address the recommendations and the potential patient effort needed to complete the recommendations. Results: For the 28,742 patients, there were 127,273 uncompleted recommendations identified for population health management (mean recommendations per patient 4.36, standard deviation of 2.65, range of 0–17 recommendations per patient). The age group with the most incomplete recommendations was age of 50–65 years with 5.5 recommendations per patient. The 18–35 years age group had the fewest incomplete recommendations with 2.6 per patient. Across all age groups, initiation of these recommendations required high-level input (physician, nurse practitioner, or physician’s assistant) in 28%. To completely adhere to recommended services, a 1000-patient cross-section cohort would require a total of 464 procedures and 1956 lab tests. Conclusion: Providers and patients face a daunting number of tasks necessary to meet guideline-generated recommendations. We will need new approaches to address the burgeoning numbers of uncompleted recommendations.

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.

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.

E-consultations are being offered within clinic walls as an option for specialist advice without a face-to-face consultation appointment. In a six month time frame, nearly 100% of primary care internists and family medicine providers in a multispecialty practice had used an e-consultation at least once. Specialists also used e-consultations for advice from other specialists. E-consultations were often questions about interpreting images or laboratory tests, or questions about management of chronic conditions such as osteoporosis, hypertension, or headaches. Although e-consultations were offered as an alternative to face-to-face specialty consultations, 1,111 of 5,334 e-consultations eventually did receive face-to-face appointments in the same specialty. Within 30 days of the e-consultation 11.5% had a specialty face-to-face visit and 17.7% had seen a specialist face-to-face within 90 days of the e-consultation. The conversions of e-consultations to face-to-face consultations depended on the specialty providing the e-consultation (fewer for gastroenterology and infectious disease), patient distance from the clinic (fewer for international patients and those living greater than 800 kilometers from the clinic), and experience of specialist responding to the e-consultation (lower conversions for specialists providing 15 or more e-consultations).

An e-consultation is an asynchronous consultation performed by a specialist without a face-to-face patient visit. E-consultations have been available to primary care providers at the Mayo Clinic for several years. We reviewed e-consultations performed by specialists at the Mayo Clinic for the first six months of 2013. We included only “internal” e-consultations, originating from within the Rochester practice. During the study period a total of 3242 e-consultations were completed at the Mayo Clinic. After excluding those relating to patients who did not give research consent, 3008 e-consultations remained. We categorized our internal e-consultations into eight types. The most frequently used types were the first e-consultation processes to be implemented: the primary care to specialist e-consultation and the specialist to specialist e-consultation, accounting for 74% of the total. As these two types of e-consultation became widely used, the staff discovered that the e-consultation process could be adapted to meet specific practice needs and six more e-consultation types emerged. For example, intra-specialty e-consultations and surgical e-consultations accounted for 16% of the total. E-consultations appear to have improved access to specialists, and they are integrated into care processes when timely expert opinions are needed. As e-consultations evolve, it will be important to develop a standard, well-defined terminology to compare outcomes of these processes across practices.