Electronic Pill Bottles or Bidirectional Text Messaging to Improve Hypertension Medication Adherence (Way 2 Text): a Randomized Clinical Trial.

BACKGROUND: Poor medication adherence contributes to inadequate control of hypertension. However, the value of adherence monitoring is unknown. OBJECTIVE: To evaluate the impact of monitoring adherence with electronic pill bottles or bidirectional text messaging on improving hypertension control. DESIGN: Three-arm pragmatic randomized controlled trial. PATIENTS: One hundred forty-nine primary care patients aged 18-75 with hypertension and text messaging capabilities who were seen at least twice in the prior 12 months with at least two out-of-range blood pressure (BP) measurements, including the most recent visit. INTERVENTIONS: Patients were randomized in a 1:2:2 ratio to receive (1) usual care, (2) electronic pill bottles for medication adherence monitoring (pill bottle), and (3) bidirectional text messaging for medication adherence monitoring (bidirectional text). MAIN MEASURES: Change in systolic BP during the final 4-month visit compared with baseline. KEY RESULTS: At the 4-month follow-up visit, mean (SD) change values in systolic blood pressure were - 4.7 (23.4) mmHg in usual care, - 4.3 (21.5) mmHg in the pill bottle arm, and - 4.6 (19.8) mmHg in the text arm. There was no significant change in systolic blood pressure between control and the pill bottle arm (p = 0.94) or the text messaging arm (p = 1.00), and the two intervention arms did not differ from each other (p = 0.93). CONCLUSIONS: Despite good measured adherence, neither feedback with electronic pill bottles nor bidirectional text messaging about medication adherence improved blood pressure control. Adherence to prescribed medications was not improved enough to affect BP control or it was not the primary driver of poor control. TRIAL REGISTRATION: clinicaltrials.gov (NCT02778542).

Impact of an interprofessional transition of care service on 30-day hospital reutilizations.

The objective of this study was to evaluate the impact of an interprofessional Transitions of Care (TOC) service on 30-day hospital reutilization inclusive of hospital readmissions and ED visits. This was a retrospective cohort study including patients discharged from an academic medical center between September 2013 and October 2014. Patients scheduled for a hospital follow-up visit in the post-acute care clinic (PACC) were included in the intervention group and patients without a post-discharge interprofessional TOC service were included in the comparison group. The intervention included a hospital follow-up visit with an interprofessional healthcare team. The primary composite outcome was hospital reutilization, defined as a hospital readmission or ED visit within 30 days of the discharge date. Overall, 330 patients were included in each group. In the intention-to-treat analysis, the primary composite outcome was not significantly different between groups (16.97% vs. 19.39%, P = 0.4195) whereas in the per-protocol analysis (all patients who showed to their PACC appointment), the primary outcome was significantly different in favor of the intervention group (9.28% vs. 19.39%, P = 0.0009). When components were analyzed separately, there was a statistically significant difference in favor of intervention group for hospital readmissions, but there was no difference for ED visits. This study demonstrates that an outpatient interprofessional TOC service with patient engagement from a team of nurses, pharmacists, physicians, and social workers may reduce 30-day hospital readmissions but may not impact 30-day ED visits.

Social Incentives and Gamification to Promote Weight Loss: The LOSE IT Randomized, Controlled Trial.

BACKGROUND: Social networks influence obesity patterns, but interventions to leverage social incentives to promote weight loss have not been well evaluated. OBJECTIVE: To test the effectiveness of gamification interventions designed using insights from behavioral economics to enhance social incentives to promote weight loss. DESIGN: The Leveraging Our Social Experiences and Incentives Trial (LOSE IT) was a 36-week randomized, controlled trial with a 24-week intervention and 12-week follow-up. PARTICIPANTS: One hundred and ninety-six obese adults (body mass index ≥ 30) comprising 98 two-person teams. INTERVENTIONS: All participants received a wireless weight scale, used smartphones to track daily step counts, formed two-person teams with a family member or friend, and selected a weight loss goal. Teams were randomly assigned to control or one of two gamification interventions for 36 weeks that used points and levels to enhance collaborative social incentives. One of the gamification arms also had weight and step data shared regularly with each participant's primary care physician (PCP). MAIN OUTCOME MEASURES: The primary outcome was weight loss at 24 weeks. Secondary outcomes included weight loss at 36 weeks. KEY RESULTS: At 24 weeks, participants lost significant weight from baseline in the control arm (mean: - 3.9 lbs; 95% CI: - 6.1 to - 1.7; P < 0.001), the gamification arm (mean: - 6.6 lbs; 95% CI: - 9.4 to - 3.9; P < 0.001), and the gamification arm with PCP data sharing (mean: - 4.8 lbs; 95% CI: - 7.4 to - 2.3; P < 0.001). At 36 weeks, weight loss from baseline remained significant in the control arm (mean: - 3.5 lbs; 95% CI: - 6.1 to - 0.8; P = 0.01), the gamification arm (mean: - 6.3 lbs; 95% CI: - 9.2 to - 3.3; P < 0.001), and the gamification arm with PCP data sharing (mean: - 5.2 lbs; 95% CI: - 8.5 to - 2.0; P < 0.01). However, in the main adjusted model, there were no significant differences in weight loss between each of the intervention arms and control at either 12, 24, or 36 weeks. CONCLUSIONS: Using digital health devices to track behavior with a partner led to significant weight loss through 36 weeks, but the gamification interventions were not effective at promoting weight loss when compared to control. TRIAL REGISTRATION: clinicaltrials.gov Identifier: 02564445.

Using Active Choice Within the Electronic Health Record to Increase Influenza Vaccination Rates.

BACKGROUND: Despite the benefits of influenza vaccination, each year more than half of adults in the United States do not receive it. OBJECTIVE: To evaluate the association between an active choice intervention in the electronic health record (EHR) and changes in influenza vaccination rates. DESIGN: Observational study. PATIENTS: Adults eligible for influenza vaccination with a clinic visit at one of three internal medicine practices at the University of Pennsylvania Health System between September 2010 and March 2013. INTERVENTION: The EHR confirmed patient eligibility during the clinic visit and, upon accessing the patient chart, prompted the physician and their medical assistant to actively choose to "accept" or "cancel" an order for the influenza vaccine. MAIN MEASURES: Change in influenza vaccination order rates at the intervention practice compared to two control practices for the 2012-2013 flu season, comparing trends during the prior two flu seasons adjusting for time trends and patient and clinic visit characteristics. KEY RESULTS: The sample (n = 45,926 patients) was 62.9% female, 35.9% white, and 54.4% black, with a mean age of 50.2 years. Trends were similar between practices during the 2 years in the pre-intervention period. Vaccination rates increased in both groups in the post-intervention year, but the intervention practice using active choice had a significantly greater increase than the control (adjusted difference-in-difference: 6.6 percentage points; 95% CI, 5.1-8.1; P < 0.001), representing a 37.3% relative increase compared to the pre-intervention period. More than 99.9% (9938/9941) of orders placed during the study period resulted in vaccination. CONCLUSIONS: Active choice through the EHR was associated with a significant increase in influenza vaccination rates.

Behavioral interventions to improve population health outreach for hepatitis C screening: randomized clinical trial.

OBJECTIVE: To evaluate whether opt out framing, messaging incorporating behavioral science concepts, or electronic communication increases the uptake of hepatitis C virus (HCV) screening in patients born between 1945 and 1965. DESIGN: Pragmatic randomized controlled trial. SETTING: 43 primary care practices from one academic health system (Philadelphia, PA, USA) between April 2019 and May 2020. PARTICIPANTS: Patients born between 1945 and 1965 with no history of screening and at least two primary care visits in the two years before the enrollment period. INTERVENTIONS: This multilevel trial was divided into two studies. Substudy A included 1656 eligible patients of 17 primary care clinicians who were randomized in a 1:1 ratio to a mailed letter about HCV screening (letter only), or a similar letter with a laboratory order for HCV screening (letter+order). Substudy B included the remaining 19 837 eligible patients followed by 417 clinicians. Active electronic patient portal users were randomized 1:5 to receive a mailed letter about HCV screening (letter), or an electronic patient portal message with similar content (patient portal); inactive patient portal users were mailed a letter. In a factorial design, patients in substudy B were also randomized 1:1 to receive standard content (usual care), or content based on principles of social norming, anticipated regret, reciprocity, and commitment (behavioral content). MAIN OUTCOME MEASURES: Proportion of patients who completed HCV testing within four months. RESULTS: 21 303 patients were included in the intention-to-treat analysis. Among the 1642 patients in substudy A, 19.2% (95% confidence interval 16.5% to 21.9%) completed screening in the letter only arm and 43.1% (39.7% to 46.4%) in the letter+order arm (P<0.001). Among the 19 661 patients in substudy B, 14.6% (13.9% to 15.3%) completed screening with usual care content and 13.6% (13.0% to 14.3%) with behavioral science content (P=0.06). Among active patient portal users, 17.8% (16.0% to 19.5%) completed screening after receiving a letter and 13.8% (13.1% to 14.5%) after receiving a patient portal message (P<0.001). CONCLUSIONS: Opt out framing and effort reduction by including a signed laboratory order with outreach increased screening for HCV. Behavioral science messaging content did not increase uptake, and mailed letters achieved a greater response rate than patient portal messages. TRIAL REGISTRATION: ClinicalTrials.gov NCT03712553.

Phenotyping physician practice patterns and associations with response to a nudge in the electronic health record for influenza vaccination: A quasi-experimental study.

BACKGROUND: Health systems routinely implement changes to the design of electronic health records (EHRs). Physician behavior may vary in response and methods to identify this variation could help to inform future interventions. The objective of this study was to phenotype primary care physician practice patterns and evaluate associations with response to an EHR nudge for influenza vaccination. METHODS AND FINDINGS: During the 2016-2017 influenza season, 3 primary care practices at Penn Medicine implemented an active choice intervention in the EHR that prompted medical assistants to template influenza vaccination orders for physicians to review during the visit. We used latent class analysis to identify physician phenotypes based on 9 demographic, training, and practice pattern variables, which were obtained from the EHR and publicly available sources. A quasi-experimental approach was used to evaluate response to the intervention relative to control practices over time in each of the physician phenotype groups. For each physician latent class, a generalized linear model with logit link was fit to the binary outcome of influenza vaccination at the patient visit level. The sample comprised 45,410 patients with a mean (SD) age of 58.7 (16.3) years, 67.1% were white, and 22.1% were black. The sample comprised 56 physicians with mean (SD) of 24.6 (10.2) years of experience and 53.6% were male. The model segmented physicians into groups that had higher (n = 41) and lower (n = 15) clinical workloads. Physicians in the higher clinical workload group had a mean (SD) of 818.8 (429.1) patient encounters, 11.6 (4.7) patient appointments per day, and 4.0 (1.1) days per week in clinic. Physicians in the lower clinical workload group had a mean (SD) of 343.7 (129.0) patient encounters, 8.0 (2.8) patient appointments per day, and 3.1 (1.2) days per week in clinic. Among the higher clinical workload group, the EHR nudge was associated with a significant increase in influenza vaccination (adjusted difference-in-difference in percentage points, 7.9; 95% CI, 0.4-9.0; P = .01). Among the lower clinical workload group, the EHR nudge was not associated with a significant difference in influenza vaccination rates (adjusted difference-in-difference in percentage points, -1.0; 95% CI, -5.3-5.8; P = .90). CONCLUSIONS: A model-based approach categorized physician practice patterns into higher and lower clinical workload groups. The higher clinical workload group was associated with a significant response to an EHR nudge for influenza vaccination.

Seeking Consensus on the Terminology of Value-Based Transformation Through use of a Delphi Process.

Collaboration among diverse stakeholders involved in the value transformation of health care requires consistent use of terminology. The objective of this study was to reach consensus definitions for the terms value-based care, value-based payment, and population health. A modified Delphi process was conducted from February 2017 to July 2017. An in-person panel meeting was followed by 3 rounds of surveys. Panelists anonymously rated individual components of definitions and full definitions on a 9-point Likert scale. Definitions were modified in an iterative process based on results of each survey round. Participants were a panel of 18 national leaders representing population health, health care delivery, academic medicine, payers, patient advocacy, and health care foundations. Main measures were survey ratings of definition components and definitions. At the conclusion of round 3, consensus was reached on the following definition for value-based payment, with 13 of 18 panelists (72.2%) assigning a high rating (7- 9) and 1 of 18 (5.6%) assigning a low rating (1-3): "Value-based payment aligns reimbursement with achievement of value-based care (health outcomes/cost) in a defined population with providers held accountable for achieving financial goals and health outcomes. Value-based payment encourages optimal care delivery, including coordination across healthcare disciplines and between the health care system and community resources, to improve health outcomes, for both individuals and populations." The iterative process elucidated specific areas of agreement and disagreement for value-based care and population health but did not reach consensus. Policy makers cannot assume uniform interpretation of other concepts underlying health care reform efforts.

Association of Primary Care Clinic Appointment Time With Clinician Ordering and Patient Completion of Breast and Colorectal Cancer Screening.

Importance: As the clinic day progresses, clinicians may fall behind schedule and experience decision fatigue. However, the association of time of day with cancer screening rates is unknown. Objective: To evaluate the association of primary care clinic appointment time with clinician ordering and patient completion of breast and colorectal cancer screening. Design, Setting, and Participants: Retrospective, quality improvement study of 33 primary care practices in Pennsylvania and New Jersey from September 1, 2014, to August 31, 2016. Participants included adults eligible for breast or colorectal cancer screening. Data analysis was conducted from April 24, 2018, to November 8, 2018. Exposures: Clinic appointment time during each patient's first primary care physician visit in the study period. Main Outcomes and Measures: Primary outcome was clinician ordering of the screening test during the visit. Secondary outcome was patient completion of the tests within 1 year of the visit. Results: Among the 19 254 patients eligible for breast cancer screening, the mean (SD) age was 60.2 (6.9) years; 19 254 (100%) were female, 11 682 (60.7%) were white, and 5495 (28.5%) were black. Screening test order rates were highest at 8 am at 63.7%, decreased throughout the morning to 48.7% at 11 am, increased to 56.2% at noon, and then decreased to 47.8% at 5 pm (adjusted odds ratio [OR] for overall trend, 0.94; 95% CI, 0.93-0.96; P < .001). Trends in screening test completion rates were similar beginning at 33.2% at 8 am and decreasing to 17.8% at 5 pm (adjusted OR, 0.95; 95% CI, 0.94-0.97; P < .001). Among the 33 468 patients eligible for colorectal cancer screening, the mean (SD) age was 59.6 (7.4) years; 18 672 (55.8%) were female, 22 157 (66.2%) were white, and 7296 (21.8%) were black. Screening test order rates were 36.5% at 8 am, decreased to 31.3% by 11 am, increased at noon to 34.4%, and then decreased to 23.4% at 5 pm (adjusted OR, 0.94; 95% CI, 0.93-0.95; P < .001). Trends in screening test completion rates were similar beginning at 28.0% at 8 am and decreasing to 17.8% at 5 pm (adjusted OR, 0.97; 95% CI, 0.96-0.98; P < .001). Conclusions and Relevance: Clinician ordering of cancer screening tests significantly decreased as the clinic day progressed. Patient completion of cancer screening tests within 1 year of the visit was also lower as the primary care appointment time was later in the day. Future interventions targeting improvements in cancer screening should consider how time of day may influence these behaviors.

Association of an Active Choice Intervention in the Electronic Health Record Directed to Medical Assistants With Clinician Ordering and Patient Completion of Breast and Colorectal Cancer Screening Tests.

Importance: Early cancer detection can lead to improved outcomes, but cancer screening tests are often underused. Objective: To evaluate the association of an active choice intervention in the electronic health record directed to medical assistants with changes in clinician ordering and patient completion of breast and colorectal cancer screening tests. Design, Setting, and Participants: A retrospective quality improvement study was conducted among 69 916 patients eligible for breast or colorectal cancer screening at 25 primary care practices at the University of Pennsylvania Health System between September 1, 2014, and August 31, 2017. Data analysis was conducted from January 21 to July 8, 2019. Interventions: From 2016 to 2017, 3 primary care practices at the University of Pennsylvania Health System implemented an active choice intervention in the electronic health record that prompted medical assistants to inform patients about cancer screening during check-in and template orders for clinicians to review during the visit. Main Outcomes and Measures: The primary outcome was clinician ordering of cancer screening tests. The secondary outcome was patient completion of cancer screening tests within 1 year of the primary care visit. Results: The sample eligible for breast cancer screening comprised 26 269 women with a mean (SD) age of 60.4 (6.9) years; 15 873 (60.4%) were white and 7715 (29.4%) were black. The sample eligible for colorectal cancer screening comprised 43 647 patients with a mean (SD) age of 59.4 (7.5) years; 24 416 (55.9%) were women, 19 231 (44.1%) were men, 29 029 (66.5%) were white, and 9589 (22.0%) were black. For breast cancer screening, the intervention was associated with a significant increase in clinician ordering of tests (22.2 percentage points; 95% CI, 17.2-27.6 percentage points; P < .001) but no change in patient completion (0.1 percentage points; 95% CI, -4.0 to 4.3 percentage points; P = .45). For colorectal cancer screening, the intervention was associated with a significant increase in clinician ordering of tests (13.7 percentage points; 95% CI, 8.0-18.9 percentage points; P < .001) but no change in patient completion (1.0 percentage points; 95% CI, -3.2 to 4.6 percentage points; P = .36). Conclusions and Relevance: An active choice intervention in the electronic health record directed to medical assistants was associated with a significant increase in clinician ordering of breast and colorectal cancer screening tests. However, it was not associated with a significant change in patient completion of either cancer screening test during a 1-year follow-up.

Variations in Influenza Vaccination by Clinic Appointment Time and an Active Choice Intervention in the Electronic Health Record to Increase Influenza Vaccination.

Importance: Influenza vaccination rates in the United States are suboptimal near 40%, but little is known about variations in care based on clinic appointment time. Objectives: To compare differences in influenza vaccination rates by clinic appointment time and to evaluate the association of an active choice intervention in the electronic health record with changes in vaccination rates. Design, Setting, and Participants: Retrospective, quality improvement study of 11 primary care practices at the University of Pennsylvania Health System from September 1, 2014, to March 31, 2017. Participants included adults eligible for influenza vaccination. Data analysis was conducted from October 20, 2017, to March 9, 2018. Interventions: During the 2016 to 2017 influenza season, 3 primary care practices at the University of Pennsylvania Health System implemented an active choice intervention in the electronic health record that prompted medical assistants to ask patients about influenza vaccination during check-in and template vaccination orders for clinicians to review during the visit. Main Outcomes and Measures: Influenza vaccination rates. Results: The sample comprised 96 291 patients with a mean (SD) age of 56.2 (17.0) years; 41 865 (43.5%) were men, 61 813 (64.2%) were white, and 23 802 (24.7%) were black. Among all practices across all 3 years, vaccination rates were approximately 44% from 8 am to 10 am, declined to 41.2% by 11 am and 38.3% at noon, increased to 40.2% at 1 pm, and then declined to 34.3% at 3 pm and 32.0% at 4 pm (P < .001 for adjusted linear trend). For the 3 years, vaccination rates were 46.9%, 47.2%, and 45.6% at control practices and 49.7%, 52.2%, and 59.3% at intervention practices, respectively. In adjusted analyses, compared with control practices over time, the active choice intervention was associated with a significant 9.5-percentage point increase in vaccination rates (95% CI, 4.1-14.3; P < .001). Vaccination rates increased similarly across times of the day. Conclusions and Relevance: Influenza vaccination rates significantly declined as the clinic day progressed. The active choice intervention was associated with a significant increase in influenza vaccination rates that were similar in magnitude throughout the day.

Patient and physician predictors of hyperlipidemia screening and statin prescription.

OBJECTIVES: Appropriate lipid management has been demonstrated to reduce cardiovascular events, but rates of hyperlipidemia screening and statin therapy are suboptimal. We aimed to evaluate patient and physician predictors of guideline-concordant hyperlipidemia screening and statin prescription. STUDY DESIGN: Retrospective study of patients with primary care provider (PCP) visits from 2014 to 2016 at the University of Pennsylvania Health System. METHODS: Data on patients, screening orders, and prescriptions were obtained from the electronic health record. Multivariate logistic regression models were fit to binary outcomes of lipid screening and statin prescription. RESULTS: Among 97,189 eligible patients, 79.9% had an order for hyperlipidemia screening. In adjusted models, significant patient predictors of greater odds of having screening ordered included a history of diabetes (odds ratio [OR], 1.19; 95% CI, 1.10-1.29; P <.001) or hypertension (OR, 1.16; 95% CI, 1.10-1.23; P <.001). Significant provider predictors of lower odds of having screening ordered were being a resident PCP (OR, 0.63; 95% CI, 0.43-0.93; P = .021) or being trained in family medicine (OR, 0.37; 95% CI, 0.30-0.47; P <.001). Among 40,845 eligible patients, 56.1% were prescribed a statin. In adjusted models, significant patient predictors of greater odds of being prescribed a statin were if they had a history of diabetes (OR, 2.70; 95% CI, 2.32-3.13; P <.001) or clinical cardiovascular disease (OR, 2.26; 95% CI, 1.85-2.76; P <.001). Significant provider predictors of lower odds of being prescribed a statin were being a physician assistant (OR, 0.65; 95% CI, 0.52-0.81; P <.001) or female (OR, 0.82; 95% CI, 0.70-0.96; P = .01). CONCLUSIONS: Both patient and provider factors significantly predicted guideline-concordant care for hyperlipidemia screening and statin therapy.

Effect of an Automated Patient Dashboard Using Active Choice and Peer Comparison Performance Feedback to Physicians on Statin Prescribing: The PRESCRIBE Cluster Randomized Clinical Trial.

Importance: Statins are not prescribed to approximately 50% of patients who could benefit from them. Objective: To evaluate the effectiveness of an automated patient dashboard using active choice framing with and without peer comparison feedback on performance to nudge primary care physicians (PCPs) to increase guideline-concordant statin prescribing. Design, Setting, and Participants: This 3-arm cluster randomized clinical trial was conducted from February 21, 2017, to April 21, 2017, at 32 practice sites in Pennsylvania and New Jersey. Participants included 96 PCPs and 4774 patients not previously receiving statin therapy. Data were analyzed from April 25, 2017, to June 16, 2017. Interventions: Primary care physicians in the 2 intervention arms were emailed a link to an automated online dashboard listing their patients who met national guidelines for statin therapy but had not been prescribed this medication. The dashboard included relevant patient information, and for each patient, PCPs were asked to make an active choice to prescribe atorvastatin, 20 mg, once daily, atorvastatin at another dose, or another statin or not prescribe a statin and select a reason. The dashboard was available for 2 months. In 1 intervention arm, the email to PCPs also included feedback on their statin prescribing rate compared with their peers. Primary care physicians in the usual care group received no interventions. Main Outcomes and Measures: Statin prescription rates. Results: Patients had a mean (SD) age of 62.4 (8.3) years and a mean (SD) 10-year atherosclerotic cardiovascular disease risk score of 13.6 (8.2); 2625 (55.0%) were male, 3040 (63.7%) were white, and 1318 (27.6%) were black. In the active choice arm, 16 of 32 PCPs (50.0%) accessed the patient dashboard, but only 2 of 32 (6.3%) signed statin prescription orders. In the active choice with peer comparison arm, 12 of 32 PCPs (37.5%) accessed the patient dashboard and 8 of 32 (25.0%) signed statin prescription orders. Statins were prescribed in 40 of 1566 patients (2.6%) in the usual care arm, 116 of 1743 (6.7%) in the active choice arm, and 117 of 1465 (8.0%) in the active choice with peer comparison arm. In the main adjusted model, compared with usual care, there was a significant increase in statin prescribing in the active choice with peer comparison arm (adjusted difference in percentage points, 5.8; 95% CI, 0.9-13.5; P = .008), but not in the active choice arm (adjusted difference in percentage points, 4.1; 95% CI, -0.8 to 13.1; P = .11). Conclusions and Relevance: An automated patient dashboard using both active choice framing and peer comparison feedback led to a modest but significant increase in guideline-concordant statin prescribing rates. Trial Registration: ClinicalTrials.gov Identifier: NCT03021759.

Using active choice within the electronic health record to increase physician ordering and patient completion of high-value cancer screening tests.

BACKGROUND: High value screening tests such as colonoscopy and mammography can improve early cancer detection but are often underutilized. METHODS: We evaluated an active choice intervention using the electronic health record (EHR) to confirm patient eligibility for colonoscopy or mammography during the patient's clinic visit and prompt the physician and his/her medical assistant to actively choose to "accept" or "cancel" an order for it. We fit multivariate logistic regression models using a difference-in-differences approach to evaluate changes in physician ordering and patient completion of colonoscopy and mammography at the intervention practice compared to two control practices, adjusting for time trends, patient and clinic visit characteristics. RESULTS: The sample comprised 7560 patients due for colonoscopy and 8337 patients due for mammography. Pre-intervention trends between practices did not differ. In the adjusted models, compared to the control group over time, the intervention practice had a significant increase in ordering of colonoscopy (11.8% points, 95% CI: 8.0-15.6, P<0.001) and mammography (12.4% points, 95% CI: 8.7-16.2, P<0.001). There was a significant increase in patient completion of colonoscopy (3.5% points, 95% CI: 1.1-5.9, P<0.01), but no change in mammography (2.2% points, 95% CI: -1.0 to 5.5, P=0.18). CONCLUSIONS: Active choice through the EHR was associated with an increase in physician ordering of colonoscopy and mammography. The intervention was also associated with an increase in patient completion of colonoscopy but no change in patient completion of mammography.

Obtaining patient feedback at point of service using electronic kiosks.

BACKGROUND: Engaging patients in their healthcare is a goal of healthcare reform. Obtaining sufficient, reliable patient feedback about their experiences in an office encounter has been a challenge. OBJECTIVE: To determine the feasibility of collecting feedback from patients regarding their office encounter at the point of care using touch screen kiosk technology in an urban primary care clinic. METHODS: We analyzed response rate, ease of use, provider data, and condition-specific data. The study was conducted over a 45-day period at 1 internal medicine academic teaching practice. Providers, staff, and a sponsor-supported monitor directed patients to use the kiosk after an office visit. RESULTS: A total of 1923 surveys were completed from 3850 office visits (50%). There was no appreciable impact on office flow in terms of wait time, checkout procedures, or visit with provider. Characteristics of patients completing the surveys were similar to practice demographics of patients with an office visit during the study period in terms of sex, but differed by age and race. Small but statistically significant differences were seen among patient ratings of resident versus attending physicians. Patients with depression were less likely than patients with diabetes, chronic low back pain, or asthma to report that they had set personal goals to manage their condition. CONCLUSION: This technology represents an important advance in our ability to capture the patient's opinion regarding quality and practice improvement initiatives, and has the potential for directly engaging patients in their care.

A primary care intervention for weight loss: results of a randomized controlled pilot study.

Most primary care providers (PCPs), constrained by time and resources, cannot provide intensive behavioral counseling for obesity. This study evaluated the effect of using medical assistants (MAs) as weight loss counselors. The study was a randomized controlled trial conducted in two primary care offices at an academic medical center. Patients (n = 50) had a BMI of 27-50 kg/m(2) and no contraindications to weight loss. They were randomized to quarterly PCP visits and weight loss materials (Control group) or to the same approach combined with eight visits with a MA over 6 months (Brief Counseling). Outcomes included change in weight and cardiovascular risk factors (glucose, lipids, blood pressure, and waist circumference). Patients in the Brief Counseling and Control groups lost 4.4 +/- 0.6 kg (5.1 +/- 0.7% of initial weight) and 0.9 +/- 0.6 kg (1.0 +/- 0.7%), respectively, at month 6 (P < 0.001). There were no significant differences between groups for changes in cardiovascular risk factors. Brief Counseling patients regained weight between month 6 and month 12, when MA visits were discontinued. Attrition was 10% after 6 months and 6% after 12 months. Brief Counseling by MAs induced significant weight loss during 6 months. Office-based obesity treatment should be tested in larger trials and should include weight loss maintenance counseling.