Chaudhry, Rajeev

Background Menopause-related symptoms are common but undertreated. Digital tools that support symptom assessment and patient education may improve engagement. Objective To assess whether a previsit digital menopause symptom assessment and education tool increased treatment initiation and patient engagement compared to an educational pamphlet. Design Women aged 45–55 years with upcoming primary care appointments were invited to participate. Those with a total Menopause Rating Scale (MRS) score ≥5 indicating bothersome menopause symptoms were included. Participants were randomized to a digital menopause symptom assessment tool or a digital educational pamphlet and completed a post-visit survey (NCT06919887). Participants Of the 14,726 women invited to participate between May 12, 2025, and December 5, 2025, there were 653 (4.4%) enrolled that met eligibility criteria. Interventions Randomized to an mHealth app called emmii or a standard digital menopause educational pamphlet. Main measures Menopause treatment initiation within 30 days of the clinical visit was assessed by electronic health data. Baseline and post-visit surveys were collected assessing patient experience, engagement, and perceived usefulness. Rates of menopause hormone therapy (HT) prescribing were compared with a reference population of nonparticipants aged 45–55 who had a primary care appointment at Mayo Clinic during the same period and received no study education materials. Key results Of 653 participants enrolled and randomized, 383 completed both the baseline and post-surveys (171 intervention and 212 control). HT initiation among study participants was significantly higher than in the reference population of women (9.3% vs. 4.4%, p < 0.001). There was no difference in HT initiation between the intervention (9.1%) and control groups (9.6%) ( p = 0.83). Initiation rates were 3.8% and 4.5%, respectively ( p = 0.64) for nonhormone therapies. Conclusions Among women with bothersome menopause symptoms, those who underwent previsit symptom screening and received educational materials had higher rates of HT initiation than women who did not participate in the study. These findings suggest that structured menopause education may improve recognition and management of menopause symptoms in routine clinical practice.

Bulk messaging is an important population health tool used to engage patients in preventive care and chronic disease management, yet little is known about optimal formatting of the communication. One factor to consider is whether the signatory of the patient-facing communication is the patient’s own Primary Care Provider (PCP) or a generic care team signature. In this quasi-randomized, non-blinded study we compared identical generic bulk outreach messages directed toward patients with an upcoming appointment and invited them to self-schedule a Medicare Annual wellness visit prior to their scheduled PCP appointment. Twenty-eight PCPs (1582 patients) were assigned to the PCP signature group, and 22 PCPs (1289 patients) to the care team signature group. The primary outcome was patient engagement, defined as a reply to the outreach message. Demographic, utilization, and rates of reading the bulk outreach message were similar between groups. Reply rates were significantly higher in the PCP signature group compared with the care team signature group (39.2% vs. 25.2%; odds ratio 1.86; P < 0.001). These findings suggest that using a patient’s own PCP signature in bulk outreach can meaningfully increase engagement, likely leveraging the trust inherent in established PCP–patient relationships. These results may inform health system leaders and population health teams seeking to optimize digital outreach strategies.

Abstract Objectives We aimed to improve the operational efficiency of clinical staff, including physicians and allied health professionals, in the previsit review of patients by implementing a disease-focused dashboard within the electronic health record system. The dashboard was tailored to the unique requirements of the clinic and patient population. Methods A prospective quality improvement study was conducted at an accredited pulmonary hypertension (PH) clinic within a large academic center, staffed by two full time physicians and two allied health professionals. Physicians' review time before and after implementation of the PH dashboard was measured using activity log data derived from an EHR database. The review time for clinic staff was measured through direct observation, with review method—either conventional or newly implemented dashboard—randomly assigned. Results Over the study period, the median number of patients reviewed by physicians per day increased slightly from 5.50 (interquartile range [IQR]: 1.35) before to 5.95 (IQR: 0.85) after the implementation of the PH dashboard (p = 0.535). The median review time for the physicians decreased with the use of the dashboard, from 7.0 minutes (IQR: 1.55) to 4.95 minutes (IQR: 1.35; p < 0.001). Based on the observed timing of 70 patient encounters among allied clinical staff, no significant difference was found for experienced members (4.65 minutes [IQR: 2.02] vs. 4.43 minutes [IQR: 0.69], p = 0.752), while inexperienced staff saw a significant reduction in review time after familiarization with the dashboard (5.06 minutes [IQR: 1.51] vs. 4.12 minutes [IQR: 1.99], p = 0.034). Subjective feedback highlighted the need for further optimization of the dashboard to align with the workflow of allied health staff to achieve similar efficiency benefits. Conclusion A disease-focused dashboard significantly reduced physician previsit review time while that for clinic staff remained unchanged. Validation studies are necessary with our patient populations to explore further qualitative impacts on patient care efficiency and long-term benefits on workflow.

Abstract Objective Menopause symptoms affect quality of life and financial well-being but are often unaddressed in primary care clinics. Therefore, we evaluated the extent of menopause symptom documentation in electronic health records (EHRs) by primary health care professionals. Methods We retrospectively reviewed adult women who reported moderate or higher vasomotor symptoms on a Mayo Clinic survey conducted from March 1, 2021, through June 30, 2021. We then assessed adequacy of menopause symptom documentation in the EHRs of these women who had primary care visits during the survey period. We reviewed the percentage of documented vasomotor symptoms from May 1, 2019, through May 1, 2021. Results In the Mayo Clinic Health System—Northwest Wisconsin Region, 229 women self-reported moderate or higher vasomotor symptoms in the Mayo Clinic survey. Although only 23% of these women had vasomotor symptoms listed in the EHR clinical problem lists, 60% of these women had vasomotor symptoms documented in their clinic notes from the primary care visit. Approximately 6% of women reported hormone therapy use for management of menopause symptoms, and nearly 15% reported use of nonhormone prescription therapies for vasomotor symptoms. Conclusions A greater proportion of women in our study had EHR documentation of bothersome menopause symptoms than those reported in other studies, but vasomotor symptoms remain generally untreated. We need better methods for identifying midlife women with bothersome menopause symptoms in primary care clinics so that appropriate treatment options, including hormone therapy, can be discussed and offered.

Objective: Clinical decision support systems (CDDSs) in the electronic medical record (EMR) have been implemented in primary care settings to identify patients due for cancer screening tests, while functioning as a real time reminder system. There is little known about primary care providers (PCPs) perspective or user acceptance of CDSS. The purpose of this study was to investigate primary care provider perceptions of utilizing CDSS alerts in the EMR to promote increased screening rates for breast cancer, cervical cancer, and colorectal cancer. Methods: An electronic survey was administered to PCPs in a Midwest Health Institution community internal medicine practice from September 25, 2019 through November 27, 2019. Results: Among 37 participants (9 NP/Pas and 28 MD/DOs), the NP/PA group was more likely to agree that alerts were helpful (50%; P-value = .0335) and the number of alerts (89%; P = .0227) in the EMR was appropriate. The NP/PA group also was more likely to find alerts straightforward to use (78%, P = .0239). Both groups agreed about feeling comfortable using the health maintenance alerts ( MD/DO = 79%; NP/PA = 100%). Conclusion: CDSSs can promote and facilitate ordering of cancer screening tests. The use of technology can promptly identify patients due for a test and act as a reminder to the PCP. PCPs identify these alerts to be a beneficial tool in the EMR when they do not interrupt workflow and provide value to patient care. More work is needed to identify factors that could optimize alerts to be even more helpful, particularly to MD/DO groups.

Background: Childhood obesity is associated with dyslipidemia, fatty liver disease, and type 2 diabetes. Expert guidelines recommend screening for these conditions in children with obesity. Aims and objectives: The objective of the study was to compare rates of laboratory screening for dyslipidemia, fatty liver disease, and type 2 diabetes in children with obesity prior to and following implementation of a point-of-care decision support tool. Methods: We performed a retrospective record review of children with body mass index (BMI) ≥95th percentile for age and gender (age 7–18 years) undergoing well-child/sports examination visits. Multivariable logistic regression models were used to adjust for patient and provider confounders. Results: There was no increase in the rates of screening following implementation of the point-of-care decision support tool. Tests were more likely to be recommended in children with severe obesity and in females. Conclusions: The implementation of a point-of-care decision support tool was not associated with improvement in screening rates for dyslipidemia, fatty liver disease, and type 2 diabetes for children with obesity. Further strategies are needed to improve rates of screening for obesity-related comorbid conditions in children with obesity.

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 Automated individualized risk prediction tools linked to electronic health records ( EHR s) are not available for management of patients with peripheral arterial disease. The goal of this study was to create a prognostic tool for patients with peripheral arterial disease using data elements automatically extracted from an EHR to enable real‐time and individualized risk prediction at the point of care. Methods and Results A previously validated phenotyping algorithm was deployed to an EHR linked to the Rochester Epidemiology Project to identify peripheral arterial disease cases from Olmsted County, MN, for the years 1998 to 2011. The study cohort was composed of 1676 patients: 593 patients died over 5‐year follow‐up. The c‐statistic for survival in the overall data set was 0.76 (95% confidence interval [CI], 0.74–0.78), and the c‐statistic across 10 cross‐validation data sets was 0.75 (95% CI, 0.73–0.77). Stratification of cases demonstrated increasing mortality risk by subgroup (low: hazard ratio, 0.35 [95% CI, 0.21–0.58]; intermediate‐high: hazard ratio, 2.98 [95% CI, 2.37–3.74]; high: hazard ratio, 8.44 [95% CI, 6.66–10.70], all P <0.0001 versus the reference subgroup). An equation for risk calculation was derived from Cox model parameters and β estimates. Big data infrastructure enabled deployment of the real‐time risk calculator to the point of care via the EHR . Conclusions This study demonstrates that electronic tools can be deployed to EHR s to create automated real‐time risk calculators to predict survival of patients with peripheral arterial disease. Moreover, the prognostic model developed may be translated to patient care as an automated and individualized real‐time risk calculator deployed at the point of care.