Improving management of the genitourinary syndrome of menopause: evaluation of a health system-based, cluster-randomized intervention.

BACKGROUND: Genitourinary symptoms are common in postmenopausal women and adversely affect the quality of life. National surveys and data collected from our healthcare system indicate that postmenopausal women with the genitourinary syndrome of menopause often fail to receive appropriate diagnosis or treatment. OBJECTIVE: To promote greater detection and treatment of the genitourinary syndrome of menopause, we created and tested a clinician-focused health system intervention that included clinician education sessions and a suite of evidence-based electronic health record tools. STUDY DESIGN: Using a cluster-randomized design, we allocated primary care (16) and gynecology (6) clinics to the intervention or control group. From September to November 2014, we provided training about the diagnosis and treatment of genitourinary syndrome of menopause in face-to-face presentations at each intervention clinic and in an online video. We developed clinical decision support tools in the electronic health record that contained an evidence-based, point-of-care knowledge resource, a standardized order set, and a checklist of patient education materials for the patient's after visit summary. The tools aimed to facilitate accurate diagnostic coding and prescribing (SmartSet, SmartRx) along with relevant patient information (SmartText). Clinicians who only performed visits at control clinics received no training or notification about the tools. Our primary outcome was vulvovaginal diagnoses made at well visits for women at the age of 55 years and older from November 15, 2014 to November 15, 2015. We also assessed urinary diagnoses, vaginal estrogen prescriptions, and use of the electronic tools. There was departmental support for the intervention but no prioritization within the healthcare system to incentivize change. RESULTS: In the 1-year period, 386 clinicians performed 14,921 well visits for women at the age of 55 years and older. Among the 190 clinicians who performed well visits in the intervention clinics, 109 (57.4%) completed either in-person or online educational training. The proportion of visits that included a vulvovaginal (7.2% vs 5.8%; odds ratio, 1.27; 95% confidence interval, 0.65-2.51) or urinary diagnosis (2.5% vs 3.1%; odds ratio, 0.79; 95% confidence interval, 0.55-1.13) or vaginal estrogen prescription (4.5% vs 3.7%; odds ratio, 1.24; 95% confidence interval, 0.63-2.46) did not differ between study arms. There was a significant interaction for primary care and gynecology, which revealed more vulvovaginal diagnoses by gynecology but not primary care intervention clinics (odds ratio, 1.63; 95% confidence interval, 1.15-2.31), but there was no significant interaction for prescriptions. Clinicians in the intervention clinics were more likely to use decision support tools than those in control clinics-SmartSet (22.2% vs 1.5%; odds ratio, 18.8; 95% confidence interval, 5.5-63.8) and SmartText for patient information (38.0% vs 24.4%; odds ratio, 1.91; 95% confidence interval, 1.10-3.34). A per-protocol analysis revealed similar findings. CONCLUSION: Overall, the intervention did not lead to more diagnoses or prescription therapy for postmenopausal genitourinary symptoms but did result in greater distribution of patient information. Gynecology clinicians were more likely to address genitourinary symptoms generally and were more likely to make a vulvovaginal diagnosis after the intervention. Further efforts for improving care should consider ongoing clinician education beginning with enhanced menopause curricula in residency training. Additional interventions to consider include greater access for postmenopausal women to gynecologic care, addressing treatment barriers, and development of national performance metrics.

Bias in Self-reported Prepregnancy Weight Across Maternal and Clinical Characteristics.

OBJECTIVES: Prepregnancy body mass index (BMI) and gestational weight gain (GWG) are known determinants of maternal and child health; calculating both requires an accurate measure of prepregnancy weight. We compared self-reported prepregnancy weight to measured weights to assess reporting bias by maternal and clinical characteristics. METHODS: We conducted a retrospective cohort study among pregnant women using electronic health records (EHR) data from Kaiser Permanente Northwest, a non-profit integrated health care system in Oregon and southwest Washington State. We identified women age ≥ 18 years who were pregnant between 2000 and 2010 with self-reported prepregnancy weight, ≥ 2 measured weights between ≤ 365-days-prior-to and ≤ 42-days-after conception, and measured height in their EHR. We compared absolute and relative difference between self-reported weight and two "gold-standards": (1) weight measured closest to conception, and (2) usual weight (mean of weights measured 6-months-prior-to and ≤ 42-days-after conception). Generalized-estimating equations were used to assess predictors of misreport controlling for covariates, which were obtained from the EHR or linkage to birth certificate. RESULTS: Among the 16,227 included pregnancies, close agreement (± 1 kg or ≤ 2%) between self-reported and closest-measured weight was 44% and 59%, respectively. Overall, self-reported weight averaged 1.3 kg (SD 3.8) less than measured weight. Underreporting was higher among women with elevated BMI category, late prenatal care entry, and pregnancy outcome other than live/stillbirth (p < .05). Using self-reported weight, BMI was correctly classified for 91% of pregnancies, but ranged from 70 to 98% among those with underweight or obesity, respectively. Results were similar using usual weight as gold standard. CONCLUSIONS FOR PRACTICE: Accurate measure of prepregnancy weight is essential for clinical guidance and surveillance efforts that monitor maternal health and evaluate public-health programs. Identification of characteristics associated with misreport of self-reported weight can inform understanding of bias when assessing the influence of prepregnancy BMI or GWG on health outcomes.

Decrease in unnecessary vitamin D testing using clinical decision support tools: making it harder to do the wrong thing.

OBJECTIVE: To evaluate the impact of clinical decision support (CDS) tools on rates of vitamin D testing. Screening for vitamin D deficiency has increased in recent years, spurred by studies suggesting vitamin D's clinical benefits. Such screening, however, is often unsupported by evidence and can incur unnecessary costs. MATERIALS AND METHODS: We evaluated how rates of vitamin D screening changed after we implemented 3 CDS tools in the electronic health record (EHR) of a large health plan: (1) a new vitamin D screening guideline, (2) an alert that requires clinician acknowledgement of current guidelines to continue ordering the test (a "hard stop"), and (3) a modification of laboratory ordering preference lists that eliminates shortcuts. We assessed rates of overall vitamin D screening and appropriate vitamin D screening 6 months pre- and post-intervention. RESULTS: Vitamin D screening rates decreased from 74.0 tests to 24.2 tests per 1000 members ( P < .0001). The proportion of appropriate vitamin D screening tests increased from 56.2% to 69.7% ( P < .0001), and the proportion of inappropriate screening tests decreased from 43.8% pre-implementation to 30.3% post-implementation ( P < .0001). DISCUSSION: To our knowledge, this is the first demonstration of how CDS can reduce rates of inappropriate vitamin D screening. We used 3 straightforward, inexpensive, and replicable CDS approaches. We know of no previous research on the impact of removing options from a preference list. CONCLUSION: Similar approaches could be used to reduce unnecessary care and decrease costs without reducing quality of care.