RESUMO
OBJECTIVE: To validate algorithms identifying uterine perforations and intrauterine device (IUD) expulsions and to ascertain availability of breastfeeding status at the time of IUD insertion. STUDY DESIGN AND SETTING: Four health care systems with electronic health records (EHRs) participated: Kaiser Permanente Northern California (KPNC), Kaiser Permanente Southern California (KPSC), Kaiser Permanente Washington (KPWA), and Regenstrief Institute (RI). The study included women ≤50 years of age with an IUD insertion. Site-specific algorithms using structured and unstructured data were developed and a sample validated by EHR review. Positive predictive values (PPVs) of the algorithms were calculated. Breastfeeding status was assessed in a random sample of 125 women at each research site with IUD placement within 52 weeks postpartum. RESULTS: The study population included 282,028 women with 325,582 IUD insertions. The PPVs for uterine perforation were KPNC 77%, KPSC 81%, KPWA 82%, and RI 47%; PPVs for IUD expulsion were KPNC 77%, KPSC 87%, KPWA 68%, and RI 37%. Across all research sites, breastfeeding status at the time of IUD insertion was determined for 94% of those sampled. CONCLUSIONS: Algorithms with a high PPV for uterine perforation and IUD expulsion were developed at 3 of the 4 research sites. Breastfeeding status at the time of IUD insertion could be determined at all research sites. Our findings suggest that a study to evaluate the associations of breastfeeding and postpartum IUD insertions with risk of uterine perforation and IUD expulsion can be successfully conducted retrospectively; however, automated application of algorithms must be supplemented with chart review for some outcomes at one research site due to low PPV.
RESUMO
OBJECTIVE: To evaluate an online disease management system supporting patients with uncontrolled type 2 diabetes. MATERIALS AND METHODS: Engaging and Motivating Patients Online With Enhanced Resources for Diabetes was a 12-month parallel randomized controlled trial of 415 patients with type 2 diabetes with baseline glycosylated hemoglobin (A1C) values ≥7.5% from primary care sites sharing an electronic health record. The intervention included: (1) wirelessly uploaded home glucometer readings with graphical feedback; (2) comprehensive patient-specific diabetes summary status report; (3) nutrition and exercise logs; (4) insulin record; (5) online messaging with the patient's health team; (6) nurse care manager and dietitian providing advice and medication management; and (7) personalized text and video educational 'nuggets' dispensed electronically by the care team. A1C was the primary outcome variable. RESULTS: Compared with usual care (UC, n=189), patients in the intervention (INT, n=193) group had significantly reduced A1C at 6 months (-1.32% INT vs -0.66% UC; p<0.001). At 12 months, the differences were not significant (-1.14% INT vs -0.95% UC; p=0.133). In post hoc analysis, significantly more INT patients had improved diabetes control (>0.5% reduction in A1C) than UC patients at 12 months (69.9 (95% CI 63.2 to 76.5) vs 55.4 (95% CI 48.4 to 62.5); p=0.006). CONCLUSIONS: A nurse-led, multidisciplinary health team can manage a population of diabetic patients in an online disease management program. INT patients achieved greater decreases in A1C at 6 months than UC patients, but the differences were not sustained at 12 months. More INT than UC patients achieved improvement in A1C (>0.5% decrease). Trial registered in clinical trials.gov: #NCT00542204.
