Stroebel, Robert J.

Objective: This article examines how visual exposure to patients predicts patient-related communication among staff members. Background: Communication among healthcare professionals private from patients, or backstage communication, is critical for staff teamwork and patient care. While patients and visitors are a core group of users in healthcare settings, not much attention has been given to how patients’ presence impacts staff communication. Furthermore, many healthcare facilities provide team spaces for improved staff teamwork, but the privacy levels of team areas significantly vary. Method: This article presents an empirical study of four team-based primary care clinics where staff communication and teamwork are important. Visual exposure levels of the clinics were analyzed, and their relationships to staff members’ concerns for having backstage communication, including preferred and nonpreferred locations for backstage communication, were investigated. Results: Staff members in clinics with less visual exposure to patients reported lower concerns about having backstage communication. Staff members preferred talking in team areas that were visually less exposed to patients in the clinic, but, within team areas, the level of visual exposure did not matter. On the other hand, staff members did not prefer talking in visually exposed areas such as corridors in the clinic and visually exposed areas within team spaces. Conclusions: Staff members preferred talking in team areas, and they did not prefer talking in visually exposed areas. These findings identified visually exposed team areas as a potentially uncomfortable environment, with a lack of agreement between staff members’ preferences toward where they had patient-related communication.

ObjectivesPerformance measurement is used by health care providers, payers, and patients. Historically accomplished using administrative data, registries are used increasingly to track and improve care. We assess how measured diabetes care quality differs when calculated using claims versus registry.Data Sources/Study SettingCross‐sectional analysis of administrative claims and electronic health records (EHRs) of patients in a multispecialty integrated health system in 2012 (n = 368,883).Study DesignWe calculated percent of patients attaining glycohemoglobin <8.0 percent, LDL cholesterol <100 mg/dL, blood pressure <140/90 mmHg, and nonsmoking (D4) in cohorts, identified by Medicare Accountable Care Organization/Minnesota Community Measures (ACO‐MNCM; claims‐based), Healthcare Effectiveness Data and Information Set (HEDIS; claims‐based), and registry (EHR‐based).Data Collection/Extraction MethodsClaims were linked to EHR to create a dataset of performance‐eligible patients.Principal FindingsACO‐MNCM, HEDIS, and registry identified 6,475, 6,989, and 6,425 measurement‐eligible patients. Half were common among the methods; discrepancies were due to attribution, age restriction, and encounter requirements. D4 attainment was lower in ACO‐MNCM (36.09 percent) and HEDIS (37.51 percent) compared to registry (43.74 percent) cohorts.ConclusionsRegistry‐ and claims‐based performance measurement methods identify different patients, resulting in different rates of quality metric attainment with implications for innovative population health management.

Introduction: Secure messages and electronic visits (“e-visits”) through patient portals provide patients with alternatives to face-to-face appointments, telephone contact, letters, and e-mails. Limited information exists on how portal messaging impacts face-to-face visits in primary care. Materials and Methods: We conducted a retrospective cohort study of 2,357 primary care patients who used electronic messaging (both secure messages and e-visits) on a patient portal. Face-to-face appointment frequencies (visits/year) of each patient were calculated before and after the first message in a matched-pairs analysis. We analyzed visit frequencies with and without adjustments for a first message surge in visits, and we examined subgroups of high message utilizers and long-term users. Results: Primary care patients who sent at least one message (secure message or e-visit) had a mean of 2.43 (standard deviation [SD] 2.3) annual face-to-face visits before the first message and 2.47 (SD 2.8) after, a nonsignificant difference ( p =0.45). After adjustment for a first message surge in visits, no significant visit frequency differences were observed (mean, 2.35 annual visits per patient both before and after first message; p =0.93). Subgroup analysis also showed no significant change in visit frequency for patients with higher message utilization or for those who had used the messaging feature longer. Conclusions: No significant change in face-to-face visit frequency was observed following implementation of portal messaging. Secure messaging and e-visits through a patient portal may not result in a change of adult primary care face-to-face visits.

Telemedicine practitioners are familiar with multiple barriers to delivering care at a distance. Licensing and reimbursement barriers are well known and are being addressed at national and state levels by the American Telemedicine Association. Another telemedicine barrier comes in the form of quality measures for diabetes. Minnesota medical practices are currently being compared on the proportion of their patients with diabetes who have attained goals for blood pressure, low-density lipoprotein cholesterol, and hemoglobin A1C. The quality measure for blood pressure specifically excludes measurements taken by the patient, thus precluding blood pressure telemonitoring as a way to meet the blood pressure goal. To counter this barrier, advocacy in telemedicine is needed so that telemonitoring as a data collection tool is included in quality measures.

The United States Preventive Services Task Force and the National Osteoporosis Foundation recommend routine osteoporosis screening for women aged 65 years or older. Previous studies have shown that the use of a clinical decision-support tool significantly improves screening rates. In a recently published study, a statistically significant improvement was found in the screening rates for eligible women with use of the tool. To evaluate whether a clinical decision-support tool independently predicts completion of osteoporosis screening tests and to identify predictors of screening completion, we examined the records of 2462 female patients who were eligible for osteoporosis screening but had no prior baseline screening and who were seen in our primary care practices in 2007 and 2008. Patient and provider characteristics and clinic visit type were identified, and their association with screening test completion was statistically analyzed using both univariate and multivariate models. Screening completion rates increased significantly from 2007 to 2008. Factors associated with increased likelihood of screening completion included race, marital status, residence, presence of comorbidity (cancer, rheumatologic disease), and the year and type of visit. Screening was less likely for women aged 80 years or older. The use of a point-of-care decision-support tool not only improved osteoporosis screening rates significantly but appeared to be an independent predictor of screening completion. It potentially can facilitate the systematic and effective delivery of preventive health services to patients in the primary care setting. ( Population Health Management 2011;14:243–247)

Introduction: The H1N1 (subtype hemagglutinin 1 neuraminidase 1) influenza pandemic of 2009 was associated with a large increase in demand for primary care office visits. However, many patients with H1N1 symptoms or exposure could be assessed and treated with telephone protocols. Methods: Specific H1N1 influenza telephone protocols were developed by Mayo Clinic physicians using Centers for Disease Control recommendations. Using symptom calls to a primary care practice in the United States, we captured nurse telephone triage recommendations, telephone antiviral prescriptions, and what callers would have done without telephone advice. We retrospectively analyzed all symptom calls from July 2009 through January 2010. Results: Call volume was 5,596 calls monthly during the peak influenza months, which was 56% above the monthly average of 3,595 calls for the nonpeak months ( p < 0.001). The calls during October 2009 were 111% over the nonpeak months ( p < 0.001). In October 2009, telephone triage nurses gave 412 prescriptions for antivirals accounting for 5.4% of calls and 39% of all telephonic prescriptions for that month. In the peak H1N1 month of October, there were 1,522 callers who intended to stay home for their care. For the same month, triage nurses suggested 3,250 of the callers stay home. For an October 2009 appointment capacity of 35,126 visit slots, a potential 5% capacity was preserved. Conclusions: A telephone triage solution for the acute demands of influenza H1N1 demonstrated how patients had needs met telephonically while preserving medical access for others.