Chaudhry, Rajeev

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.

Background: A clinical decision support system (CDSS) for cervical cancer screening identifies patients due for routine cervical cancer screening. Yet, high-risk patients who require more frequent screening or earlier follow-up to address past abnormal results are not identified. We aimed to assess the effect of a complex CDSS, incorporating national guidelines for high-risk patient screening and abnormal result management, its implementation to identify patients overdue for testing, and the outcome of sending a targeted recommendation for follow-up. Materials and Methods: At three primary care clinics affiliated with an academic medical center, a reminder recommending an appointment for Papanicolaou (Pap) testing or Pap and human papillomavirus cotesting was sent to high-risk women aged 18 through 65 years (intervention group) identified by CDSS as overdue for testing. Historical control patients, who did not receive a reminder, were identified by CDSS 1 year before the date when reminders were sent to the intervention group. Test completion rates were compared between the intervention and control groups through a generalized estimating equation extension. Results: Across the three sites, the average completion rate of recommended follow-up testing was significantly higher in the intervention group at 23.7% (61/257) than the completion rate at 3.3% (17/516) in the control group ( p < 0.001). Conclusions: A CDSS with enhanced capabilities to identify high-risk women due for cervical cancer testing beyond routine screening intervals, with subsequent patient notification, has the potential to decrease cervical precancer and cancer by improving adherence to guideline-compliant follow-up and needed treatment.

Introduction Clinical practice guidelines facilitate optimal clinical practice. Point of care access, interpretation and application of such guidelines, however, is inconsistent. Informatics-based tools may help clinicians apply guidelines more consistently. We have developed a novel clinical decision support tool that presents guideline-relevant information and actionable items to clinicians at the point of care. We aim to test whether this tool improves the management of hyperlipidaemia, atrial fibrillation and heart failure by primary care clinicians. Methods/analysis Clinician care teams were cluster randomised to receive access to the clinical decision support tool or passive access to institutional guidelines on 16 May 2016. The trial began on 1 June 2016 when access to the tool was granted to the intervention clinicians. The trial will be run for 6 months to ensure a sufficient number of patient encounters to achieve 80% power to detect a twofold increase in the primary outcome at the 0.05 level of significance. The primary outcome measure will be the percentage of guideline-based recommendations acted on by clinicians for hyperlipidaemia, atrial fibrillation and heart failure. We hypothesise care teams with access to the clinical decision support tool will act on recommendations at a higher rate than care teams in the standard of care arm. Ethics and dissemination The Mayo Clinic Institutional Review Board approved all study procedures. Informed consent was obtained from clinicians. A waiver of informed consent and of Health Insurance Portability and Accountability Act (HIPAA) authorisation for patients managed by clinicians in the study was granted. In addition to publication, results will be disseminated via meetings and newsletters. Trial registration number NCT02742545 .

Whether disclosure of genetic risk for coronary heart disease (CHD) influences shared decision-making (SDM) regarding use of statins to reduce CHD risk is unknown. We randomized 207 patients, age 45–65 years, at intermediate CHD risk, and not on statins, to receive the 10-year risk of CHD based on conventional risk factors alone (n=103) or in combination with a genetic risk score (n=104). A genetic counselor disclosed this information followed by a physician visit for SDM regarding statin therapy. A novel decision aid was used in both encounters to disclose the CHD risk estimates and facilitate SDM regarding statin use. Patients reported their decision quality and physician visit satisfaction using validated surveys. There were no statistically significant differences between the two groups in the SDM score, satisfaction with the clinical encounter, perception of the quality of the discussion or of participation in decision-making and physician visit satisfaction scores. Quantitative analyses of a random subset of 80 video-recorded encounters using the OPTION5 scale also showed no significant difference in SDM between the two groups. Disclosure of CHD genetic risk using an electronic health record-linked decision aid did not adversely affect SDM or patients’ satisfaction with the clinical encounter. Trial registration number NCT01936675; Results.

The concept of optimizing health care by understanding and generating knowledge from previous evidence, ie, the Learning Health-care System (LHS), has gained momentum and now has national prominence. Meanwhile, the rapid adoption of electronic health records (EHRs) enables the data collection required to form the basis for facilitating LHS. A prerequisite for using EHR data within the LHS is an infrastructure that enables access to EHR data longitudinally for health-care analytics and real time for knowledge delivery. Additionally, significant clinical information is embedded in the free text, making natural language processing (NLP) an essential component in implementing an LHS. Herein, we share our institutional implementation of a big data-empowered clinical NLP infrastructure, which not only enables health-care analytics but also has real-time NLP processing capability. The infrastructure has been utilized for multiple institutional projects including the MayoExpertAdvisor, an individualized care recommendation solution for clinical care. We compared the advantages of big data over two other environments. Big data infrastructure significantly outperformed other infrastructure in terms of computing speed, demonstrating its value in making the LHS a possibility in the near future.

Background: Advance care planning (ACP) is an instrumental mechanism aimed at preserving patient autonomy. Numerous interventions have been proposed to facilitate the implementation of ACP; however, rates of completed advance directives (ADs) are universally low. Patient electronic portal messaging is a newer tool in patient–provider communication which has not been studied as a method to promote ACP. In this study, we hypothesized that the use of ACP-specific patient electronic messages would increase rates of AD completion in patients aged 65 years and older in an academic primary care practice. Methods: All primary care patients, aged 65+, who had previously enrolled in a patient electronic messaging system, within an academic primary care practice, were included for randomization. Two hundred patients were randomized to receive an electronic message. The primary outcome was the proportion of patients in each group who completed an AD, 3 months after intervention. Secondary outcomes included clinical utility of the completed ADs and proportion of patients who viewed their electronic messages. Results: The intervention group completed an AD 5.5% of the time when compared to 2% in the control group (odds ratio 3.2 [1.6-6.3]). Up to 74.5% of patients opened their electronic messages. Conclusion: Among primary care patients aged 65 years and older, use of AD-specific electronic messaging statistically significantly increased the rate of AD completion, but the absolute number of completed AD remained relatively low. These data suggest that this valuable communication tool holds opportunities for further improvement. Older, frailer adults were more likely to complete an AD, and prompted directives were more likely to include a written expression of the individual’s health-care values and preference.

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.

Because of the complexity of cervical cancer prevention guidelines, clinicians often fail to follow best-practice recommendations. Moreover, existing clinical decision support (CDS) systems generally recommend a cervical cytology every three years for all female patients, which is inappropriate for patients with abnormal findings that require surveillance at shorter intervals. To address this problem, we developed a decision tree-based CDS system that integrates national guidelines to provide comprehensive guidance to clinicians. Validation was performed in several iterations by comparing recommendations generated by the system with those of clinicians for 333 patients. The CDS system extracted relevant patient information from the electronic health record and applied the guideline model with an overall accuracy of 87%. Providers without CDS assistance needed an average of 1 minute 39 seconds to decide on recommendations for management of abnormal findings. Overall, our work demonstrates the feasibility and potential utility of automated recommendation system for cervical cancer screening and surveillance.

Background: Clinical decision support (CDS) for primary care has been shown to improve delivery of preventive services. However, there is little evidence for efficiency of physicians due to CDS assistance. In this article, we report a pilot study for measuring the impact of CDS on the time spent by physicians for deciding on preventive services and chronic disease management. Methods: We randomly selected 30 patients from a primary care practice, and assigned them to 10 physicians. The physicians were requested to perform chart review to decide on preventive services and chronic disease management for the assigned patients. The patients assignment was done in a randomized crossover design, such that each patient received 2 sets of recommendations—one from a physician with CDS assistance and the other from a different physician without CDS assistance. We compared the physician recommendations made using CDS assistance, with the recommendations made without CDS assistance. Results: The physicians required an average of 1 minute 44 seconds, when they were they had access to the decision support system and 5 minutes when they were unassisted. Hence the CDS assistance resulted in an estimated saving of 3 minutes 16 seconds (65%) of the physicians’ time, which was statistically significant ( P < .0001). There was no statistically significant difference in the number of recommendations. Conclusion: Our findings suggest that CDS assistance significantly reduced the time spent by physicians for deciding on preventive services and chronic disease management. The result needs to be confirmed by performing similar studies at other institutions.

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.