Citations

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.

AbstractIn a continuing search for curcuminoid (CUR) compounds with antitumor activity, a novel series of heterocyclic CUR–BF2 adducts and CUR compounds based on indole, benzothiophene, and benzofuran along with their aryl pyrazoles were synthesized. Computational docking studies were performed to compare binding efficiency to target proteins involved in specific cancers, namely HER2, proteasome, VEGFR, BRAF, and Bcl‐2, versus known inhibitor drugs. The majority presented very good binding affinities, similar to, and even more favorable than those of known inhibitors. The indole‐based CUR–BF2 and CUR compounds and their bis‐thiocyanato derivatives exhibited high anti‐proliferative and apoptotic activity by in vitro bioassays against a panel of 60 cancer cell lines, more specifically against multiple myeloma (MM) cell lines (KMS11, MM1.S, and RPMI‐8226) with significantly lower IC50 values versus healthy PBMC cells; they also exhibited higher anti‐proliferative activity in human colorectal cancer cells (HCT116, HT29, DLD‐1, RKO, SW837, and Caco2) than the parent curcumin, while showing notably lower cytotoxicity in normal colon cells (CCD112CoN and CCD841CoN).

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.

Introduction: E-consultations are asynchronous text-based consultations between providers which can facilitate patient access to timely specialty care. In contrast to traditional face-to-face consults, conveying and completing recommendations of the specialist is the responsibility of the referring provider. This presents a new workflow for primary care providers who have multiple options (face-to-face, telephone, letter, secure message) to communicate the e-consultation recommendations. This study examines how primary care providers are managing this new workflow. Methods: We performed a retrospective random sampling of e-consultations with individual medical record review and classified e-consultations by type of recommendation, how recommendations were communicated to patients, and whether recommendations were carried out. Results: We randomly selected 220 e-consultations in 13 different specialties for review. In all, 85% of e-consultations contained recommendations for referring providers. Recommendations on medication(s) were most common (35%) followed by recommendations on ordering laboratory tests (29%). In all, 25% of the time e-consultants gave multiple possible courses of action for referring providers to choose from. Patient notification of recommendations was found for 192 (87%) of e-consultations with providers performing the notification 63% of the time and nursing staff performing the notification 37% of the time. The communication back to the patients included communication via nurse telephone calls (37%), provider telephone calls (23%), secure messages (24%), face-to-face visits (11%), and by written correspondence (5%). Discussion: Managing recommendations from e-consultations results in a new workflow for primary care providers. Healthcare institutions that utilize e-consults should be aware of this new workflow. Further study is needed to determine best practices for this task that is now increasing in primary care.

Introduction Under certain circumstances, e-consultations can substitute for a face-to-face consultation. A basic requirement for a successful e-consultation is that the e-consultant has access to important medical history and exam findings along with laboratory and imaging results. Knowing just what information the specialist needs to complete an e-consultation is a major challenge. This paper examines differences between specialties in their need for past information from laboratory, imaging and clinical notes. Methods This is a retrospective study of patients who had an internal e-consultation performed at an academic medical centre. We reviewed a random sample of e-consultations that occurred in the first half of 2013 for the indication for the e-consultation and whether the e-consultant reviewed data in the medical record that was older than one year to perform the e-consultation. Results Out of 3008 total e-consultations we reviewed 360 (12%) randomly selected e-consultations from 12 specialties. Questions on management (35.8%), image results (27.2%) and laboratory results (25%) were the three most common indications for e-consultation. E-consultants reviewed medical records in existence more than one year prior to the e-consultation 146 (40.6%) of the time with e-consultants in the specialties of endocrinology, haematology and rheumatology, reviewing records older than one year more than half the time. Labs (20.3%), office notes (20%) and imaging (17.8%) were the types of medical data older than one year that were reviewed the most frequently overall. Discussion Management questions appear to be the most common reason for e-consultation. E-consultants frequently reviewed historical medical data that is older than one year at the time of the e-consultation, especially in endocrinology, haematology and rheumatology specialties. Practices engaging in e-consultations that require transfer of data may want to include longer time frames of historical information for those specialties.

Background: The Apple (Cupertino, CA) HealthKit is a new telemonitoring platform that promises to make it easier for patients and healthcare institutions to collect, transmit, and store data from devices that monitor common conditions such as diabetes, hypertension, and asthma. To assess the potential use for this platform in primary care, we need to know how many Apple Healthkit users there are and if they have conditions that could benefit from telemonitoring. Materials and Methods: We examined patients in the Mayo Clinic primary care practice in Rochester, MN, who registered to connect to their Mayo Clinic medical record with Apple HealthKit. We used the primary care registry to identify users with chronic conditions of diabetes, hypertension, asthma, and depression. We also examined users for recent measurements of blood pressure, glucose, hemoglobin A1C, and cholesterol. Results: Of 98,151 patients there were 503 registrants of HealthKit. There were 95 (19%) who had hypertension, 37 (7.4%) who had diabetes, 125 (25%) who had depression, and 56 (11%) who had asthma. Overall, there were 245 (49%) who had readily telemonitorable conditions. Conclusions: Almost half of primary care Apple HealthKit registrants have conditions that could benefit from telemonitoring. This pre-installed telemonitoring platform, available on every new iPhone ® (Apple), can be used to monitor a significant number of primary care patients. However, it also has continued provider and informatics barriers that need to be addressed.

Introduction E-consultations are asynchronous, text-based consultations. The specialist e-consultant answers clinical questions in a similar way to a standard consultation but the questions and answers are sent electronically. The e-consultant has access to some or all of the medical record but does not have contact with the patient. Although e-consultations are meant to substitute for face-to-face (F2F) consultations, a significant proportion of e-consultations are converted to F2F consultations. Methods We examined e-consultation content from a sample of e-consultations that had subsequent F2F visits in the same specialty as the e-consultation within 28 days of the e-consultation. Results Out of 5115 e-consultations, there were a total of 547 (10.7%) early F2F conversions. One hundred and fifty-one e-consultations with subsequent early F2F conversions were reviewed in eight specialties. In 64% of the F2F conversions, specialists recommended the F2F consultations. In 75% there were complex diagnostic or treatment considerations. In only 1% was there a sense of medical urgency or a stated need for physical examination. Discussion E-consultations convert to F2F consultations primarily at the request of the specialist. Diagnostic and treatment complexity appear to be the main reasons. We found little evidence that patients decided independently to get a F2F visit or that specialists needed a F2F visit to perform a physical examination. Although e-consultations might not be a complete substitute for F2F consultations, they may serve as an entry level consultation that could be supplemented by a video consultation as needed for cases with more diagnostic and treatment complexity.