Search

Background: As patient-initiated messaging rises, identifying variation in message volume and its relationship to clinician workload is essential., Objective: To describe the association between variation in message volume over time and time spent on the electronic health record (EHR) outside of scheduled hours., Design: Retrospective cohort study., Participants: Primary care clinicians at Cleveland Clinic Health System., Main Measures: We categorized clinicians according to their number of quarterly incoming medical advice messages (i.e., message volume) between January 2019 and December 2021 using group-based trajectory modeling. We assessed change in quarterly messages and outpatient visits between October-December 2019 (Q4) and October-December 2021 (Q12). The primary outcome was time outside of scheduled hours spent on the EHR. We used mixed effects logistic regression to describe the association between incoming portal messages and time spent on the EHR by clinician messaging group and at the clinician level., Key Results: Among the 150 clinicians, 31% were in the low-volume group (206 messages per quarter per clinician), 47% were in the moderate-volume group (505 messages), and 22% were in the high-volume group (840 messages). Mean quarterly messages increased from 340 to 695 (p < 0.001) between Q4 and Q12; mean quarterly outpatient visits fell from 711 to 575 (p = 0.005). While time spent on the EHR outside of scheduled hours increased modestly for all clinicians, this did not significantly differ by message group. Across all clinicians, each additional 10 messages was associated with an average of 12 min per quarter of additional time spent on the EHR (p < 0.001)., Conclusions: Message volume increased substantially over the study period and varied by group. While messages were associated with additional time spent on the EHR outside of scheduled hours, there was no significant difference in time spent on the EHR between the high and low message volume groups., (© 2023. The Author(s), under exclusive licence to Society of General Internal Medicine.)

Background: The US Preventive Services Task Force recommends 25 primary preventive services for middle-aged adults, but it can be difficult to do them all., Methods: The Personalized Disease Prevention (PDP) cluster-randomized clinical trial will evaluate whether patients and their providers benefit from an evidence-based decision tool to prioritize preventive services based on their potential to improve quality-adjusted life expectancy. The decision tool will be individualized for patient risk factors and available in the electronic health record. This Phase III trial seeks to enroll 60 primary care providers (clusters) and 600 patients aged 40-75 years. Half of providers will be assigned to an intervention to utilize the decision tool with approximately 10 patients each, and half will be assigned to usual care. Mixed-methods follow-up will include collection of preventive care utilization from electronic health records, patient and physician surveys, and qualitative interviews. We hypothesize that quality-adjusted life expectancy will increase by more in patients who receive the intervention, as compared with controls., Discussion: PDP will test a novel, holistic approach to help patients and providers prioritize the delivery of preventive services, based on patient risk factors in the electronic health record., Trial Registration: ClinicalTrials.gov NCT05463887. Registered on July 19, 2022., (© 2022. The Author(s).)

Electronic health records (EHRs) offer the potential to study large numbers of patients but are designed for clinical practice, not research. Despite the increasing availability of EHR data, their use in research comes with its own set of challenges. In this article, we describe some important considerations and potential solutions for commonly encountered problems when working with large-scale, EHR-derived data for health services and community-relevant health research. Specifically, using EHR data requires the researcher to define the relevant patient subpopulation, reliably identify the primary care provider, recognize the EHR as containing episodic (i.e., unstructured longitudinal) data, account for changes in health system composition and treatment options over time, understand that the EHR is not always well-organized and accurate, design methods to identify the same patient across multiple health systems, account for the enormous size of the EHR, and consider barriers to data access. Associations found in the EHR may be nonrepresentative of associations in the general population, but a clear understanding of the EHR-based associations can be enormously valuable to the process of improving outcomes for patients in learning health care systems. In the context of building 2 large-scale EHR-derived data sets for health services research, we describe the potential pitfalls of EHR data and propose some solutions for those planning to use EHR data in their research. As ever greater amounts of clinical data are amassed in the EHR, use of these data for research will become increasingly common and important. Attention to the intricacies of EHR data will allow for more informed analysis and interpretation of results from EHR-based data sets.

Objectives: To describe associations between resident level of training, timing of medication orders, and the types of inpatient medication ordering errors made by internal medicine residents., Methods: This study reviewed all inpatient medication orders placed by internal medicine residents at a tertiary care academic medical center from July 2011 to June 2015. Medication order errors were measured by pharmacists' reporting of an error via the electronic medical record during real-time surveillance of orders. Multivariable regression models were constructed to assess associations between resident training level (postgraduate year [PGY]), medication order timing (time of day and month of year), and rates of medication ordering errors., Results: Of 1,772,462 medication orders placed by 335 residents, 68,545 (3.9%) triggered a pharmacist intervention in the electronic medical record. Overall and for each PGY level, renal dose monitoring/adjustment was the most common order error (40%). Ordering errors were less frequent during the night and transition periods versus daytime (adjusted odds ratio [aOR] 0.93, 95% confidence interval [CI] 0.91-0.96, and aOR 0.93, 95% CI 0.90-0.95, respectively). Errors were more common in July and August compared with other months (aOR 1.05, 95% CI 1.01-1.09). Compared with PGY2 residents, both PGY1 (aOR 1.06, 95% CI 1.03-1.10), and PGY3 residents (aOR 1.07, 95% CI, 1.03-1.10) were more likely to make medication ordering errors. Throughout the course of the academic year, the odds of a medication ordering error decreased by 16% (aOR 0.84, 95% CI 0.80-0.89)., Conclusions: Despite electronic medical records, medication ordering errors by trainees remain common. Additional supervision and resident education regarding medication orders may be necessary.

Introduction: The number of preventive care guidelines is rapidly increasing. It is unknown whether the number of guideline-recommended preventive services is associated with utilization., Methods: The authors used Poisson regression of 390,778 person-years of electronic medical records data from 2008 to 2015, in 80,773 individuals aged 50-75 years. Analyses considered eligibility for 11 preventive services most closely associated with guidelines: tobacco cessation; control of obesity, hypertension, lipids, or blood glucose; influenza vaccination; and screening for breast, cervical, or colorectal cancers, abdominal aortic aneurysm, or osteoporosis. The outcome was the rate of preventive care utilization over the following year. Results were adjusted for demographics and stratified by the number of disease risk factors (smoking, obesity, hypertension, hyperlipidemia, diabetes). Data were collected in 2016 and analyzed in 2017., Results: Preventive care utilization was lower when the number of guideline-recommended preventive services was higher. The adjusted rate of preventive care utilization decreased from 38.67 per 100 (95% CI=38.16, 39.18) in patients eligible for one guideline-recommended service to 31.59 per 100 (95% CI=31.29, 31.89) in patients eligible for two services and 25.43 per 100 (95% CI=24.68, 26.18) in patients eligible for six or more services (p-trend<0.001). Results were robust to disease risk factors and observed for all but two services (tobacco cessation, obesity reduction). However, for any given number of guideline-recommended services, patients with more disease risk factors had higher utilization rates., Conclusions: The rate of preventive care utilization was lower when the number of guideline-recommended services was higher. Prioritizing recommendations might improve utilization of high-value services., (Copyright © 2018 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.)

Background: The pneumonia 30-d readmission rate has been endorsed by the National Quality Forum as a quality metric. Hospital readmissions can potentially be lowered by improving in-hospital care, transitions of care, and post-discharge disease management programs. The purpose of this study was to create an accurate prediction model for determining the risk of 30-d readmission at the point of discharge., Methods: The model was created using a data set of 1,295 hospitalizations at the Cleveland Clinic Main Campus with pneumonia over 3 y. Candidate variables were limited to structured variables available in the electronic health record. The final model was compared with the Centers for Medicare and Medicaid Services (CMS) model among subjects 65 y of age and older ( n = 628) and was externally validated., Results: Three hundred thirty subjects (25%) were readmitted within 30 d. The final model contained 13 variables and had a bias-corrected C statistic of 0.74 (95% CI 0.71-0.77). Number of admissions in the prior 6 months, opioid prescription, serum albumin during the first 24 h, international normalized ratio and blood urea nitrogen during the last 24 h were the predictor variables with the greatest weight in the model. In terms of discriminative performance, the Cleveland Clinic model outperformed the CMS model on the validation cohort (C statistic 0.69 vs 0.60, P = .042)., Conclusions: The proposed risk prediction model performed better than the CMS model. Accurate readmission risk prediction at the point of discharge is feasible and can potentially be used to focus post-acute care interventions in a high-risk group of patients., Competing Interests: The authors have disclosed no conflicts of interest., (Copyright © 2018 by Daedalus Enterprises.)

Background: Extending medical assistants and nursing roles to include in-visit documentation is a recent innovation in the age of electronic health records. Despite the use of these clinical scribes, little is known regarding interactions among and perspectives of the involved parties: physicians, clinical scribes, and patients., Objective: The purpose of this project is to describe perspectives of physicians, clinical scribes, and patients regarding clinical scribes in primary care., Design: We used qualitative content analysis, using Interpretive Description of semi-structured audio-recorded in-person and telephone interviews., Participants: Participants included 18 physicians and 17 clinical scribes from six healthcare systems, and 36 patients from one healthcare system., Key Results: Despite physician concerns regarding terminology within notes, physicians, clinical scribes, and patients perceived more detailed notes because of real-time documentation by scribes. Most patients were comfortable with the scribe's presence and perceived increased attention from their physicians. Clinical scribes also performed more active roles during a patient visit, leading to formation of positive scribe-patient relationships. The resulting shift in workflow, however, led to stress. Our theoretical model for successful physician-scribe teams emphasizes the importance of interpersonal aspects such as communication, mutual respect, and adaptability, as well as system level support such as training and staffing., Conclusions: Both interpersonal fit between physician and scribe, and system level support including adequate training, transition time, and staffing support are necessary for successful use of clinical scribes. Future directions for research regarding clinical scribes include study of care continuity, scribe medical knowledge, and scribe burnout.

Background: Effective shared decision-making (SDM) tools for use during clinical encounters are available, but, outside of study settings, little is known about clinician use of these tools in practice. Objective: To describe real-world use of an SDM encounter tool for statin prescribing, Statin Choice, embedded into the workflow of an electronic health record. Design: Cross-sectional study. Participants: Clinicians and their statin-eligible patients who had outpatient encounters between January 2020 and June 2021 in Cleveland Clinic Health System. Main Measures: Clinician use of Statin Choice was recorded within the Epic record system. We categorized each patient's 10-year atherosclerotic cardiovascular disease risk into low (< 5%), borderline (5–7.5%), intermediate (7.5–20%), and high (≥ 20%). Other patient factors included age, sex, insurance, and race. We used mixed effects logistic regression to assess the odds of using Statin Choice for statin-eligible patients, accounting for clustering by clinician and site. We generated a residual intraclass correlation coefficient (ICC) to characterize the impact of the clinician on Statin Choice use. Key Results: Statin Choice was used in 7% of 68,505 eligible patients. Of 1047 clinicians, 48% used Statin Choice with ≥ 1 patient, and these clinicians used it with a median 9% of their patients (interquartile range: 3–22%). In the mixed effects logistic regression model, patient age (adjusted OR per year: 1.04; 95%CI 1.03–1.04) and 10-year ASVCD risk (aOR for 5–7.5% versus < 5% risk: 1.28; 95%CI: 1.14–1.44) were associated with use of Statin Choice. Black versus White race was associated with a lower odds of Statin Choice use (aOR: 0.83; 95%CI: 0.73–0.95), as was female versus male sex (aOR: 0.83; 95%CI: 0.76–0.90). The model ICC demonstrated that 53% of the variation in use of Statin Choice was clinician-driven. Conclusions: Patient factors, including race and sex, were associated with clinician use of Statin Choice; half the variation in use was attributable to individual clinicians. [ABSTRACT FROM AUTHOR]

Aim: To characterize factors associated with the receipt of anti‐obesity medication (AOM) prescription and fill. Materials and Methods: This retrospective cohort study used electronic health records from 1 January 2015 to 30 June 2023, in a large health system in Ohio and Florida. Adults with a body mass index ≥30 kg/m2 who attended ≥1 weight‐management programme or had an initial AOM prescription between 1 July 2015 and 31 December 2022, were included. The main measures were a prescription for an AOM (naltrexone‐bupropion, orlistat, phentermine‐topiramate, liraglutide 3.0 mg and semaglutide 2.4 mg) and an AOM fill during the study follow‐up. Results: We identified 50 678 adults, with a mean body mass index of 38 ± 8 kg/m2 and follow‐up of 4.7 ± 2.4 years. Only 8.0% of the cohort had AOM prescriptions and 4.4% had filled prescriptions. In the multivariable analyses, being a man, Black, Hispanic and other race/ethnicity (vs. White), Medicaid, traditional Medicare, Medicare Advantage, self‐pay and other insurance types (vs. private insurance) and fourth quartile of the area deprivation index (vs. first quartile) were associated with lower odds of a new prescription. Hispanic ethnicity, being a man, Medicaid, traditional Medicare and Medicare Advantage insurance types, liraglutide and orlistat (vs. naltrexone‐buproprion) were associated with lower odds of AOM fill, while phentermine‐topiramate was associated with higher odds. Among privately insured individuals, the insurance carrier was associated with both the odds of AOM prescription and fill. Conclusions: Significant disparities exist in access to AOM both at the prescribing stage and getting the prescription filled based on patient characteristics and insurance type. [ABSTRACT FROM AUTHOR]

Background: As patient-initiated messaging rises, identifying variation in message volume and its relationship to clinician workload is essential. Objective: To describe the association between variation in message volume over time and time spent on the electronic health record (EHR) outside of scheduled hours. Design: Retrospective cohort study. Participants: Primary care clinicians at Cleveland Clinic Health System. Main Measures: We categorized clinicians according to their number of quarterly incoming medical advice messages (i.e., message volume) between January 2019 and December 2021 using group-based trajectory modeling. We assessed change in quarterly messages and outpatient visits between October–December 2019 (Q4) and October–December 2021 (Q12). The primary outcome was time outside of scheduled hours spent on the EHR. We used mixed effects logistic regression to describe the association between incoming portal messages and time spent on the EHR by clinician messaging group and at the clinician level. Key Results: Among the 150 clinicians, 31% were in the low-volume group (206 messages per quarter per clinician), 47% were in the moderate-volume group (505 messages), and 22% were in the high-volume group (840 messages). Mean quarterly messages increased from 340 to 695 (p < 0.001) between Q4 and Q12; mean quarterly outpatient visits fell from 711 to 575 (p = 0.005). While time spent on the EHR outside of scheduled hours increased modestly for all clinicians, this did not significantly differ by message group. Across all clinicians, each additional 10 messages was associated with an average of 12 min per quarter of additional time spent on the EHR (p < 0.001). Conclusions: Message volume increased substantially over the study period and varied by group. While messages were associated with additional time spent on the EHR outside of scheduled hours, there was no significant difference in time spent on the EHR between the high and low message volume groups. [ABSTRACT FROM AUTHOR]

Objective: The study's objective was to examine the percentage of patients with an initial antiobesity medication (AOM) fill who were persistent with AOM at 3, 6, and 12 months and to characterize factors associated with persistence at 12 months. Methods: This retrospective cohort study used electronic health records from January 2015 to July 2023 in a large health system in Ohio and Florida and included adults with BMI ≥30 kg/m2 who had an initial AOM prescription filled between 2015 and 2022. Results: The authors identified 1911 patients with a median baseline BMI of 38 (IQR, 34–44). Over time, 44% were persistent with AOM at 3 months, 33% at 6 months, and 19% at 12 months. Across categories of AOM, the highest 1‐year persistence was in patients receiving semaglutide (40%). Semaglutide (adjusted odds ratio [AOR] = 4.26, 95% CI: 3.04–6.05) was associated with higher odds of 1‐year persistence, and naltrexone‐bupropion (AOR = 0.68, 95% CI: 0.46–1.00) was associated with lower odds, compared with phentermine‐topiramate. Among patients who were persistent at 6 months, a 1% increase in weight loss at 6 months was associated with 6% increased odds of persistence at year 1 (AOR = 1.06, 95% CI: 1.03–1.09). Conclusions: Later‐stage persistence with AOM varies considerably based on the drug and the weight loss at 6 months. [ABSTRACT FROM AUTHOR]

Background: Early recognition and treatment of bacteremia can be lifesaving. Fever is a well-known marker of bacteremia, but the predictive value of temperature has not been fully explored. Objective: To describe temperature as a predictor of bacteremia and other infections. Design: Retrospective review of electronic health record data. Setting: A single healthcare system comprising 13 hospitals in the United States. Patients: Adult medical patients admitted in 2017 or 2018 without malignancy or immunosuppression. Main Measures: Maximum temperature, bacteremia, influenza and skin and soft tissue (SSTI) infections based on blood cultures and ICD-10 coding. Key Results: Of 97,174 patients, 1,518 (1.6%) had bacteremia, 1,392 (1.4%) had influenza, and 3,280 (3.3%) had an SSTI. There was no identifiable temperature threshold that provided adequate sensitivity and specificity for bacteremia. Only 45% of patients with bacteremia had a maximum temperature ≥ 100.4˚F (38˚C). Temperature showed a U-shaped relationship with bacteremia with highest risk above 103˚F (39.4˚C). Positive likelihood ratios for influenza and SSTI also increased with temperature but showed a threshold effect at ≥ 101.0 ˚F (38.3˚C). The effect of temperature was similar but blunted for patients aged ≥ 65 years, who frequently lacked fever despite bacteremia. Conclusions: The majority of bacteremic patients had maximum temperatures below 100.4 ˚F (38.0˚C) and positive likelihood ratios for bacteremia increased with high temperatures above the traditional definition of fever. Efforts to predict bacteremia should incorporate temperature as a continuous variable. [ABSTRACT FROM AUTHOR]

Polypharmacy, or use of multiple medications, is associated with patient factors. Less is known regarding variation in polypharmacy by individual physicians. The objective of this study was to assess patient and physician factors associated with polypharmacy among older patients. This is a cross-sectional study of patients aged ≥65 years with a primary care visit at Cleveland Clinic Health System in 2015 and their physicians. We collected patient demographics, comorbidities and current medications from the electronic health record, including potentially inappropriate medications (PIMs). We used mixed effects linear regression to estimate adjusted differences in the number of medications by patient factors. We generated adjusted prescribing rates for individual physicians and assessed differences in physician performance on quality measures by their prescribing rate. Our study included 44,570 patients who were prescribed an average of 6.8 medications (standard deviation: 4.0) by 701 physicians. Female sex, higher BMI, having Medicaid insurance, current or former smoking status, comorbidities and seeing a specialist were associated with number of medications. Age was not. Among 267 physicians who saw ≥20 study-eligible patients, the adjusted mean number of medications per patient ranged from 5.2 to 9.6. Compared to physicians who prescribed above the mean, lower prescribing physicians performed significantly better on medication reconciliation (p =.007) and hypertension control (p <.001) and prescribed fewer PIMs (p <.001). Individual physicians varied in their prescribing practices, even after adjusting for patient demographic and clinical characteristics. Interventions to reduce polypharmacy in older adults should target high prescribing physicians, as physician behavior is more actionable than patient factors. [ABSTRACT FROM AUTHOR]

Objectives: Opioid receipt during medical hospitalizations may be associated with subsequent long-term use. Studies, however, have not accounted for pain, which may explain chronic use. The objective of this study was to identify the association between opioid exposure during a medical hospitalization and use 6 to 12 months later.Methods: This was an observational cohort study using electronic health record data from 10 hospitals in the Cleveland Clinic Health System in 2016. Eligible patients were opioid-naïve adults with pain age 18 years and older, admitted to a medical service. Outcomes were opioid receipt during hospitalization and on discharge, and long-term opioid use, defined as ≥2 prescriptions for at least 30 pills 6 to 12 months posthospitalization. We estimated the odds of long-term opioid use by opioid exposure during the hospitalization. Models controlled for patient demographic and clinical characteristics, including patient-reported pain.Results: Among the 2971 patients in the sample, 64% received opioids during their hospitalization and 28% were discharged with opioids. Overall, 3% of patients had long-term use. Higher pain score was associated with greater odds of long-term use (adjusted odds ratio [aOR] per point increase 1.11; 95% confidence interval [CI] 1.03-1.19). No patient factors were associated with long-term use. Receipt of an opioid during a hospitalization only was not associated with long-term use (aOR 1.44, 95% CI 0.81-2.57), but receipt at discharge was (aOR 1.96, 95% CI 1.08-3.56).Conclusions: Although opioid receipt at discharge was associated with long-term use, the number of patients this applied to was small. Pain severity was an important predictor of long-term use and should be accounted for in future studies. [ABSTRACT FROM AUTHOR]

Background: Over one third of American adults are at high risk for developing diabetes, which can be delayed or prevented using interventions such as medical nutrition therapy (MNT) or metformin. Physicians' self-reported rates of prediabetes treatment are improving, but patterns of actual referral, prescription, and MNT visits are unknown. Objective: To characterize treatment of prediabetes in primary care. Design: We conducted a retrospective cohort study using electronic health record data. We described patterns of treatment and used multivariable logistic regression to evaluate the association of patient factors and PCP-specific treatment rate with patient treatment. Patients: We included overweight or obese outpatients who had a first prediabetes-range hemoglobin A1c (HbA1c) during 2011–2018 and had primary care provider (PCP) follow-up within a year. Main Measures: We collected patient characteristics and the following treatments: metformin prescription; referral to MNT, diabetes education, endocrinology, or bariatric medicine; and MNT visit. We did not capture within-visit physician counseling. Key Results: Of 16,713 outpatients with prediabetes, 20.4% received treatment, including metformin prescriptions (7.8%) and MNT referrals (11.3%), but only 7.4% of referred patients completed a MNT visit. The strongest predictor of treatment was the patient's PCP's treatment rate. Some PCPs never treated prediabetes, but two treated more than half of their patients; 62% had no patients complete a MNT visit. Being younger or female and having higher body mass index or HbA1c were also positively associated with treatment. Compared to white patients, black patients were more likely to receive MNT referral and less likely to receive metformin. Conclusions: Almost 80% of patients with new prediabetes never received treatment, and those who did receive referrals had very poor visit completion. Treatment rates appear to reflect provider rather than patient preferences. [ABSTRACT FROM AUTHOR]

Background: Ketogenic diets have been highlighted as a way to lose weight while experiencing reduced hunger. The protein-sparing modified fast (PSMF) induces ketosis but may be difficult to maintain.Objective: To track weight loss for individuals initiating PSMF versus all other diets (e.g., balanced, high protein) for up to 5 years.Design: Retrospective cohort study PARTICIPANTS: Adults who discussed the PSMF with a clinician between 2007 and 2014 INTERVENTION: Initiating the PSMF diet versus other diets MEASURES: The main outcome was percent weight change up to 5 years. Demographic and health data were collected using electronic health records. We fit regression models including age, sex, race, insurance, new medication prescriptions, and specialist visit to identify the effect of PSMF diet on percent weight change. We grouped patients by percent weight change at each year (≥ 5% loss, 4% loss to 4% gain, ≥ 5% gain) and used Pearson χ2 tests to compare proportions.Results: Of 1,403 eligible patients, 879 (63%) started the PSMF. The PSMF group was slightly younger (52 vs. 54 years, p < 0.01) and had a higher body mass index (41.9 kg/m2 vs. 40.4 kg/m2, p < 0.001). In the adjusted analysis, the PSMF group averaged 3% more weight loss than the other group over the 5-year follow-up (95% CI - 3.5, - 2.0, p < 0.001). PSMF patients lost more weight initially, but by year 4, there was no difference between diets (1.6% versus 1.3%, PSMF versus other diets, p = 0.12). Patients starting the PSMF were more likely to experience ≥ 5% weight loss at 1 year (55% vs 20%, p < 0.001) and 3 years (33% vs. 23% p < 0.05), but not 5 years (34% vs 29%, p = 0.16, PSMF versus other diets, respectively).Conclusions: In clinical practice, the PSMF achieves rapid weight loss in the first 6 months, but only a small percentage of patients maintained significant weight loss long term. [ABSTRACT FROM AUTHOR]

Objectives To assess prostate cancer screening practices in primary care since the initial United States Preventive Services Task Force ( USPSTF) recommendation against prostate-specific antigen ( PSA) testing for older men, and to assess primary provider variation associated with prostate cancer screening. Patients and Methods Our study population included 160 211 men aged ≥40 years with at least one visit to a primary care clinic in any of the study years in a large, integrated health system. We conducted a retrospective cohort study using electronic medical record data from January 2007 to December 2014. Yearly rates of screening PSA testing by primary care providers ( PCPs), rates of re-screening, and rates of prostate biopsies were assessed. Results Annual PSA-screening testing declined from 2007 to 2014 in all age groups, as did biennial and quadrennial screening. Yearly rates declined for men aged ≥70 years, from 22.8% to 8.9%; ages 50-69 years, from 39.2% to 20%; and ages 40-49 years, from 11% to 4.6%. Overall rates were lower for African-American (A-A) men vs non-A-A men; for men with a family history of prostate cancer, rates were similar or slightly higher than for those without a family history. PCP variation associated with ordering of PSA testing did not substantially change after the USPSTF recommendations. While the number of men screened and rates of follow-up prostate cancer screening declined in 2011-2014 compared to 2007-2010, similar re-screening rates were noted for men aged 45-75 years with initial PSA levels of <1 ng/mL or 1-3 ng/mL in both the earlier and later cohorts. For men aged >75 years with initial PSA levels of <3 ng/ mL screened in both cohorts, follow-up screening rates were similar. Rates of prostate biopsy declined for men aged ≥70 years in 2014 compared to 2007. For men who had PSA screening, rates of first prostate biopsy increased in later years for A-A men and men with a family history of prostate cancer. Conclusions Prostate cancer screening declined from 2007 to 2014 even in higher-risk groups and follow-up screening rates were not related to previous PSA level. However, rates of first prostate biopsy in men who were screened with a PSA test were higher for men with an increased risk of prostate cancer in later years. Variation in PSA testing was noted among PCPs. Future work should further explore sources of variation in screening practices and implementation of risk-based strategies for prostate cancer screening in primary care. [ABSTRACT FROM AUTHOR]

Background: Understanding resource utilization patterns among high-cost patients may inform cost reduction strategies.Objective: To identify patterns of high-cost healthcare utilization and associated clinical diagnoses and to quantify the significance of hot-spotters among high-cost users.Design: Retrospective analysis of high-cost patients in 2012 using data from electronic medical records, internal cost accounting, and the Centers for Medicare and Medicaid Services. K-medoids cluster analysis was performed on utilization measures of the highest-cost decile of patients. Clusters were compared using clinical diagnoses. We defined "hot-spotters" as those in the highest-cost decile with ≥4 hospitalizations or ED visits during the study period.Participants and Exposure: A total of 14,855 Medicare Fee-for-service beneficiaries identified by the Medicare Quality Resource and Use Report as having received 100 % of inpatient care and ≥90 % of primary care services at Cleveland Clinic Health System (CCHS) in Northeast Ohio. The highest-cost decile was selected from this population.Main Measures: Healthcare utilization and diagnoses.Key Results: The highest-cost decile of patients (n = 1486) accounted for 60 % of total costs. We identified five patient clusters: "Ambulatory," with 0 admissions; "Surgical," with a median of 2 surgeries; "Critically Ill," with a median of 4 ICU days; "Frequent Care," with a median of 2 admissions, 3 ED visits, and 29 outpatient visits; and "Mixed Utilization," with 1 median admission and 1 ED visit. Cancer diagnoses were prevalent in the Ambulatory group, care complications in the Surgical group, cardiac diseases in the Critically Ill group, and psychiatric disorders in the Frequent Care group. Most hot-spotters (55 %) were in the "frequent care" cluster. Overall, hot-spotters represented 9 % of the high-cost population and accounted for 19 % of their overall costs.Conclusions: High-cost patients are heterogeneous; most are not so-called "hot-spotters" with frequent admissions. Effective interventions to reduce costs will require a more multi-faceted approach to the high-cost population. [ABSTRACT FROM AUTHOR]

Background: Many employers offer worksite wellness programs, including financial incentives to achieve goals. Evidence supporting such programs is sparse.Objective: To assess whether diabetes and cardiovascular risk factor control in employees improved with financial incentives for participation in disease management and for attaining goals.Design: Retrospective cohort study using insurance claims linked with electronic medical record data from January 2008-December 2012.Participants: Employee patients with diabetes covered by the organization's self-funded insurance and propensity-matched non-employee patient comparison group with diabetes and commercial insurance.Intervention: Financial incentives for employer-sponsored disease management program participation and achieving goals.Main Measures: Change in glycosylated hemoglobin (HbA1c), low-density lipoprotein (LDL), systolic blood pressure (SBP), and weight.Results: A total of 1092 employees with diabetes were matched to non-employee patients. With increasing incentives, employee program participation increased (7 % in 2009 to 50 % in 2012, p < 0.001). Longitudinal mixed modeling demonstrated improved diabetes and cardiovascular risk factor control in employees vs. non-employees [HbA1c yearly change -0.05 employees vs. 0.00 non-employees, difference in change (DIC) p <0.001]. In their first participation year, employees had larger declines in HbA1c and weight vs. non-employees (0.33 vs. 0.14, DIC p = 0.04) and (2.3 kg vs. 0.1 kg, DIC p < 0.001), respectively. Analysis of employee cohorts corresponding with incentive offerings showed that fixed incentives (years 1 and 2) or incentives tied to goals (years 3 and 4) were not significantly associated with HbA1c reductions compared to non-employees. For each employee cohort offered incentives, SBP and LDL also did not significantly differ in employees compared with non-employees (DIC p > 0.05).Conclusions: Financial incentives were associated with employee participation in disease management and improved cardiovascular risk factors over 5 years. Improvements occurred primarily in the first year of participation. The relative impact of specific incentives could not be discerned. [ABSTRACT FROM AUTHOR]

Objectives: Despite the US Preventive Services Task Force recommendation against screening mammography in women younger than 50 years, rates remain high, suggesting that screening recommendations may be motivated by other factors. The objective of this study was to understand provider-reported influences on screening recommendations for women 40 to 49 years old at average risk for breast cancer.Methods: An online survey of primary care providers was conducted at four health centers in Cleveland, Ohio in 2015. Provider-reported routine recommendation of mammography for women aged 40 to 49 at average risk for breast cancer was the primary outcome. The independent measures included influence of electronic health records, national guidelines, institutional policy, patient preferences, concerns about overtreatment, concerns about false-positives, and interest in early detection on screening recommendations. We used multivariable logistic regression to estimate the odds of recommending screening by potential influences, controlling for provider characteristics and provider-assessed balance of harms and benefits of screening in this age group.Results: Of 612 providers invited, 220 completed the survey (response rate 36%); 69% routinely recommended screening and 24% believed that the harms of screening in younger women outweighed the benefits. Being influenced by institutional policy was associated with higher odds of recommending screening (odds ratio [OR] 4.19, 95% confidence interval [CI] 1.35-12.9), as was interest in early detection (OR 4.19, 95% CI 1.31-12.9). Conversely, strong influence of national guidelines was associated with a lower odds of recommending screening (OR 0.25, 95% CI 0.09-0.71). The influence of patient preferences was not associated with screening recommendation.Conclusions: Providers face competing influences on screening recommendations for younger patients, some of which may be at odds with their beliefs. Institutional policy change allowing individually tailored screening discussions may improve patient-centered care. [ABSTRACT FROM AUTHOR]

Their outpatient notes stack up well, according to this small, retrospective review. Scribes' notes were rated slightly higher in overall quality than physicians' notes-- at least for certain patient encounters. [ABSTRACT FROM AUTHOR]