Automated Phone Assessments and Hospital Readmissions.

This analysis examined the efficacy of an automated postdischarge phone assessment for reducing hospital readmissions. All patients discharged between April 1, 2013, and January 31, 2014, from a single Level 1 trauma hospital of a large regional health system center utilizing an automated postdischarge phone assessment service were contacted via automated call between 24 and 72 hours post discharge. Patients answered 5 questions assessing perceived well-being, understanding of discharge instructions and medication regimen, satisfaction, and scheduled follow-up appointments. Responses could automatically prompt health personnel to speak directly with the patient. Data analysis examined rates of hospital readmission-any admission occurring within 30 days of a previous admission-for 3 broad categories of respondents: Answering Machine, Live Answer, and Unsuccessful. There were 6867 discharges included in the analysis. Of the Live Answer patients, 3035 answered all assessment questions; 153 (5.0%) of these had a subsequent readmission. Of the 738 Unsuccessful patients, 62 (8.4%) had a subsequent readmission. Unsuccessful patients were almost 2 times more likely to have a readmission than those who answered all 5 assessment questions. Of the latter group, readmission rates were highest for those who perceived a worsening of their condition (7.4%), and lowest for those reporting no follow-up appointment scheduled (3.8%). (Population Health Management 2016;19:120-124).

Population health approach for diabetic patients with poor A1C control.

BACKGROUND: Diabetes is frequently monitored as part of quality programs and initiatives. The glycated hemoglobin (A1C) test and corresponding values are often used as quality metrics, and patients with values of 9.0% or above (9+) tend to utilize intensive resources. However, this strategy may be missing more profound opportunities to improve quality. OBJECTIVES: To analyze A1C outcomes in 2 ways: (1) year over year for patients identified as diabetic and (2) from test to test. METHODS: This study was conducted using data on more than 23,000 patients identified as having diabetes and included A1C laboratory results extracted from electronic medical records. RESULTS: The percentage of patients with poorly controlled diabetes (9+) is increasing annually, but there is sizable turnover within the population- meaning that new uncontrolled patients replace those whose outcomes improve. More than half (57.5%) of patients have their first 9+ score on their first test. And for those with a prior 9+ result, only 16.8% have 3 consecutive 9+ scores after their initial 9+ test. For all patients, the longer the interval between tests, the greater the probability that the next test result will be 9+. CONCLUSION: Instead of focusing resources only on the highly dynamic and relatively small subpopulation of patients with 9+ scores, a better option may be ensuring that all patients get regular testing according to appropriate protocols. This total population-based approach would engage all diabetic patients inside and outside practice walls to optimize provider ability to impact health outcomes.

Automation is key to managing a population's health.

Online tools for automating population health management can help healthcare organizations meet their patients' needs both during and between encounters with the healthcare system. These tools can facilitate: The use of registries to track patients' health status and care gaps. Outbound messaging to notify patients when they need care. Care team management of more patients at different levels of risk. Automation of workflows related to case management and transitions of care. Online educational and mobile health interventions to engage patients in their care. Analytics programs to identify opportunities for improvement.

Using physician-led automated communications to improve patient health.

One common thread to health care reform in the United States is an emphasis on effectively managing the care of patients with chronic conditions. A landmark study by McGlynn et al demonstrated that patients receive about 55% of the treatment they need. While technological advances allow automated means for identifying and reaching out to patients in need of treatment, few studies have evaluated their impact. The purpose of this study is to measure how an automated outreach program can be used to improve the quality of care for patients with diabetes and hypertension. Billing and electronic medical records data from a large health system in Wisconsin were studied, identifying patients with a history of diabetes and hypertension but no visits recorded in billing data related to their condition in the past 6 months. The outcomes of interest were the occurrence of a chronic care-related visit and a necessary test within 6 months of the nonadherence date. Diabetes patients who were successfully contacted were significantly more likely to have both a chronic care-related visit and an HbA1c test (odds ratio [OR] = 4.61, 95% confidence interval [CI] 3.87-5.49) than their counterparts who were not contacted. As well, hypertension patients were significantly more likely to have both a chronic care-related visit and a systolic blood pressure reading recorded in an electronic medical record (OR = 3.18, 95% CI 2.90-3.48). An automated patient identification and outreach program can be an effective means to supplement existing practice patterns to ensure that patients with chronic conditions in need of care receive the necessary treatment.