J.B. Jones

Selective serotonin reuptake inhibitors for premenstrual syndrome and premenstrual dysphoric disorder: a meta-analysis.

OBJECTIVE: To systematically review evidence of the treatment benefits of selective serotonin reuptake inhibitors (SSRIs) for symptoms related to severe premenstrual syndrome (PMS) and premenstrual dysphoric disorder. DATA SOURCES: We conducted electronic database searches of MEDLINE, Web of Science, Cochrane Library, Embase, PsycINFO, and Cinahl through March 2007, and hand-searched reference lists and pertinent journals. METHODS OF STUDY SELECTION: Studies included in the review were double-blind, randomized, controlled trials comparing an SSRI with placebo that reported a change in a validated score of premenstrual symptomatology. Studies had to report follow-up for any duration longer than one menstrual cycle among premenopausal women who met clinical diagnostic criteria for PMS or premenstrual dysphoric disorder. From 2,132 citations identified, we pooled results from 29 studies (in 19 citations) using random-effects meta-analyses and present results as odds ratios (ORs). TABULATION, INTEGRATION, AND RESULTS: Our meta- analysis, which included 2,964 women, demonstrates that SSRIs are effective for treating PMS and premenstrual dysphoric disorder (OR 0.40, 95% confidence interval [CI] 0.31–0.51). Intermittent dosing regimens were found to be less effective (OR 0.55, 95% CI 0.45–0.68) than continuous dosing regimens (OR 0.28, 95% CI 0.18–0.42). No SSRI was demonstrably better than another. The choice of outcome measurement instrument was associated with effect size estimates. The overall effect size is smaller than reported previously. CONCLUSION: Selective serotonin reuptake inhibitors were found to be effective in treating premenstrual symptoms, with continuous dosing regimens favored for effectiveness.

Effect of Financial Incentives to Physicians, Patients, or Both on Lipid Levels: A Randomized Clinical Trial

IMPORTANCE Financial incentives to physicians or patients are increasingly used, but their effectiveness is not well established. OBJECTIVE To determine whether physician financial incentives, patient incentives, or shared physician and patient incentives are more effective than control in reducing levels of low-density lipoprotein cholesterol (LDL-C) among patients with high cardiovascular risk. DESIGN, SETTING, AND PARTICIPANTS Four-group, multicenter, cluster randomized clinical trial with a 12-month intervention conducted from 2011 to 2014 in 3 primary care practices in the northeastern United States. Three hundred forty eligible primary care physicians (PCPs) were enrolled from a pool of 421. Of 25,627 potentially eligible patients of those PCPs, 1503 enrolled. Patients aged 18 to 80 years were eligible if they had a 10-year Framingham Risk Score (FRS) of 20% or greater, had coronary artery disease equivalents with LDL-C levels of 120 mg/dL or greater, or had an FRS of 10% to 20% with LDL-C levels of 140 mg/dL or greater. Investigators were blinded to study group, but participants were not. INTERVENTIONS Primary care physicians were randomly assigned to control, physician incentives, patient incentives, or shared physician-patient incentives. Physicians in the physician incentives group were eligible to receive up to

The Wired Patient: Patterns of Electronic Patient Portal Use Among Patients With Cardiac Disease or Diabetes

1024 per enrolled patient meeting LDL-C goals. Patients in the patient incentives group were eligible for the same amount, distributed through daily lotteries tied to medication adherence. Physicians and patients in the shared incentives group shared these incentives. Physicians and patients in the control group received no incentives tied to outcomes, but all patient participants received up to

Beyond the threshold: Real-time use of evidence in practice

355 each for trial participation. MAIN OUTCOMES AND MEASURES Change in LDL-C level at 12 months. RESULTS Patients in the shared physician-patient incentives group achieved a mean reduction in LDL-C of 33.6 mg/dL (95% CI, 30.1-37.1; baseline, 160.1 mg/dL; 12 months, 126.4 mg/dL); those in physician incentives achieved a mean reduction of 27.9 mg/dL (95% CI, 24.9-31.0; baseline, 159.9 mg/dL; 12 months, 132.0 mg/dL); those in patient incentives achieved a mean reduction of 25.1 mg/dL (95% CI, 21.6-28.5; baseline, 160.6 mg/dL; 12 months, 135.5 mg/dL); and those in the control group achieved a mean reduction of 25.1 mg/dL (95% CI, 21.7-28.5; baseline, 161.5 mg/dL; 12 months, 136.4 mg/dL; P < .001 for comparison of all 4 groups). Only patients in the shared physician-patient incentives group achieved reductions in LDL-C levels statistically different from those in the control group (8.5 mg/dL; 95% CI, 3.8-13.3; P = .002). CONCLUSIONS AND RELEVANCE In primary care practices, shared financial incentives for physicians and patients, but not incentives to physicians or patients alone, resulted in a statistically significant difference in reduction of LDL-C levels at 12 months. This reduction was modest, however, and further information is needed to understand whether this approach represents good value. TRIAL REGISTRATION clinicaltrials.gov Identifier: NCT01346189.

Meaningful use in practice using patient-specific risk in an electronic health record for shared decision making.

Background As providers develop an electronic health record–based infrastructure, patients are increasingly using Web portals to access their health information and participate electronically in the health care process. Little is known about how such portals are actually used. Objective In this paper, our goal was to describe the types and patterns of portal users in an integrated delivery system. Methods We analyzed 12 months of data from Web server log files on 2282 patients using a Web-based portal to their electronic health record (EHR). We obtained data for patients with cardiovascular disease and/or diabetes who had a Geisinger Clinic primary care provider and were registered “MyGeisinger” Web portal users. Hierarchical cluster analysis was applied to longitudinal data to profile users based on their frequency, intensity, and consistency of use. User types were characterized by basic demographic data from the EHR. Results We identified eight distinct portal user groups. The two largest groups (41.98%, 948/2258 and 24.84%, 561/2258) logged into the portal infrequently but had markedly different levels of engagement with their medical record. Other distinct groups were characterized by tracking biometric measures (10.54%, 238/2258), sending electronic messages to their provider (9.25%, 209/2258), preparing for an office visit (5.98%, 135/2258), and tracking laboratory results (4.16%, 94/2258). Conclusions There are naturally occurring groups of EHR Web portal users within a population of adult primary care patients with chronic conditions. More than half of the patient cohort exhibited distinct patterns of portal use linked to key features. These patterns of portal access and interaction provide insight into opportunities for electronic patient engagement strategies.

Shared decision making: using health information technology to integrate patient choice into primary care

In two landmark reports on Quality and Information Technology, the Institute of Medicine described a 21st century healthcare delivery system that would improve the quality of care while reducing its costs. To achieve the improvements envisioned in these reports, it is necessary to increase the efficiency and effectiveness of the clinical decision support that is delivered to clinicians through electronic health records at the point of care. To make these dramatic improvements will require significant changes to the way in which clinical practice guidelines are developed, incorporated into existing electronic health records (EHR), and integrated into clinicians’ workflow at the point of care. In this paper, we: 1) discuss the challenges associated with translating evidence to practice; 2) consider what it will take to bridge the gap between the current limits to use of CPGs and expectations for their meaningful use at the point of care in practices with EHRs; 3) describe a framework that underlies CDS systems which, if incorporated in the development of CPGs, can be a means to bridge this gap, 4) review the general types and adoption of current CDS systems, and 5) describe how the adoption of EHRs and related technologies will directly influence the content and form of CPGs. Achieving these objectives should result in improvements in the quality and reductions in the cost of healthcare, both of which are necessary to ensure a 21st century delivery system that consistently provides safe and effective care to all patients.

The electronic health record audit file: the patient is waiting

Quantitative risk (QR) formulas have been developed for multiple conditions but are not routinely used in clinical practice. Tests were made of the feasibility of an automated clinical care process for using QR in routine primary care. Several modifications were made to the Framingham Risk Score (FRS) and it was applied to routine care in three areas: (1) for risk-stratification, (2) patient education about care options, and (3) guidance on optimizing choice of care options. Evidence-based methods were used to convert the smoking status variable from a binary- to a continuous-scale format and to add variables for alcohol use and HbA1c. An automated protocol tested in 2008-2010 was successful for all three applications. At-risk patients (defined according to criteria from the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure [JNC]-7 or the National Cholesterol Education Program Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults [adult treatment panel/ATP-III]) were automatically identified during routine encounters. Patient-reported data were obtained (n = 1826) by touchscreen questionnaire and automatically used with electronic health record (EHR) data to calculate risks on 1068 patients who had complete data. Patients were risk-stratified. Higher-risk patients viewed an interactive web-based tool and chose options to modify risk factors. Feasibility was successful for use of the FRS in the interactive web tool.

Measuring Adherence and Outcomes in the Treatment of Patients With Multiple Sclerosis

Advances in shared decision making (SDM) have not successfully translated to practice. We describe our experience and lessons learned in translating an SDM process for primary care cardiovascular disease management. The SDM process operationalized recognized SDM elements using workflow modifications, a computerized patient questionnaire, an automated risk calculator to identify at-risk patients, a web-based tool for patients to choose interventions, automated feedback on the personalized benefits of choices, and a web-based tool for providers to view patient risk, patient choice, and expert advice. Although medication was typically the intervention resulting in the greatest risk reduction, the majority of patients preferred dietary and other lifestyle changes. Patients generally favored the opportunity to make and communicate choices. However, providers only viewed patient choice data in 20% of the encounters. Translation of the SDM process was successful for patients and the difference between patient choice and optimal risk reduction points to the importance of engaging in an SDM process. Lack of engagement by providers may be due to “alert fatigue” or to the failure of the SDM process to improve efficiency in the office visit.

Moderating Effects of Patient Characteristics on the Impact of Financial Incentives

Objective: We describe how electronic health record (EHR) audit files can be used to understand how time is spent in primary care (PC). Materials/methods: We used audit file data from the Geisinger Clinic to quantify elements of the clinical workflow and to determine how these times vary by patient and encounter factors. We randomly selected audit file records representing 36 437 PC encounters across 26 clinic locations. Audit file data were used to estimate duration and variance of: (1) time in the waiting room, (2) nurse time with the patient, (3) time in the exam room without a nurse or physician, and (4) physician time with the patient. Multivariate modeling was used to test for differences by patient and by encounter features. Results: On average, a PC encounter took 54.6 minutes, with 5 minutes of nurse time, 15.5 minutes of physician time, and the remaining 62% of the time spent waiting to see a clinician or check out. Older age, female sex, and chronic disease were associated with longer wait times and longer time with clinicians. Level of service and numbers of medications, procedures, and lab orders were associated with longer time with clinicians. Late check-in and same-day visits were associated with shorter wait time and clinician time. Conclusions: This study provides insights on uses of audit file data for workflow analysis during PC encounters. Discussion: Scalable ways to quantify clinical encounter workflow elements may provide the means to develop more efficient approaches to care and improve the patient experience.

C-B4-04: Overestimation of Population Level Medication Adherence: Bias in the MPR Calculation of Hypertensive Patients

Context Both adherence and outcomes are more difficult to measure in patients with multiple sclerosis (MS) than in patients with diseases such as hypertension, for which most medications are taken orally and surrogate outcomes (eg, blood pressure) are routinely collected. Objectives To characterize the adherence and outcomes of patients with MS within a large integrated health system and to assess the relationship between adherence and outcomes. Study Design Retrospective review of adherence and health care utilization outcomes via electronic health records and claims (2004-2013) combined with a prospective survey regarding adherence and functional outcomes (2012-2013). Methods Retrospectively, medication possession ratios were calculated, and adherence groups were compared regarding health care utilization and costs. Prospectively, patients were recruited to complete questionnaires to measure self-reported adherence (SRA) and MS-specific outcomes, including the Multiple Sclerosis Impact Scale (MSIS), the Kurtzke Expanded Disability Status Scale (EDSS), and the Treatment Satisfaction Questionnaire for Medication (TSQM). Regression was used to statistically test for differences in these outcomes among adherence groups. Results A total of 681 patients were studied. Most patients (307 of 375 [82%] in the retrospective cohort and 244 of 306 [89%] in the prospective cohort) had greater that 80% adherence to their MS medications. Mean inpatient days per patient for MS-related admissions was highest for high-adherence than for intermediate and low-adherence patients (0.52 vs 0.23 and 0.34, respectively; P<.001), but no other associations between adherence and health care utilization were found. Mean outpatient costs and total costs were lowest for the low-adherence group, suggesting that higher adherence may not guarantee cost savings overall. Patients with intermediate and high self-reported adherence showed significantly better mean scores than patients with low adherence on several MS outcomes, including EDSS (4.1 and 4.2 vs 4.8, P<.05), MSIS physical function (33 and 35 vs 41, P<.05), and TSQM Global Satisfaction (75 and 78 vs 70, P<.05). Conclusions The findings of this study indicate that patients with MS are mostly adherent to their existing treatments. Patients with greater medication adherence may have increased cost, but their physical outcomes are better. This finding may shed light on other chronic disease entities and how we view the success of treatments.