Automated telephone communication systems for preventive healthcare and management of long-term conditions

Background Automated telephone communication systems (ATCS) can deliver voice messages and collect health-related information from patients using either their telephone’s touch-tone keypad or voice recognition software. ATCS can supplement or replace telephone contact between health professionals and patients. There are four different types of ATCS: unidirectional (one-way, non-interactive voice communication), interactive voice response (IVR) systems, ATCS with additional functions such as access to an expert to request advice (ATCS Plus) and multimodal ATCS, where the calls are delivered as part of a multicomponent intervention. Objectives To assess the effects of ATCS for preventing disease and managing long-term conditions on behavioural change, clinical, process, cognitive, patient-centred and adverse outcomes. Search methods We searched 10 electronic databases (the Cochrane Central Register of Controlled Trials; MEDLINE; Embase; PsycINFO; CINAHL; Global Health; WHOLIS; LILACS; Web of Science; and ASSIA); three grey literature sources (Dissertation Abstracts, Index to Theses, Australasian Digital Theses); and two trial registries (www.controlled-trials.com; www.clinicaltrials.gov) for papers published between 1980 and June 2015. Selection criteria Randomised, cluster- and quasi-randomised trials, interrupted time series and controlled before-and-after studies comparing ATCS interventions, with any control or another ATCS type were eligible for inclusion. Studies in all settings, for all consumers/carers, in any preventive healthcare or long term condition management role were eligible. Data collection and analysis We used standard Cochrane methods to select and extract data and to appraise eligible studies. Main results We included 132 trials (N = 4,669,689). Studies spanned across several clinical areas, assessing many comparisons based on evaluation of different ATCS types and variable comparison groups. Forty-one studies evaluated ATCS for delivering preventive healthcare, 84 for managing long-term conditions, and seven studies for appointment reminders. We downgraded our certainty in the evidence primarily because of the risk of bias for many outcomes. We judged the risk of bias arising from allocation processes to be low for just over half the studies and unclear for the remainder. We considered most studies to be at unclear risk of performance or detection bias due to blinding, while only 16% of studies were at low risk. We generally judged the risk of bias due to missing data and selective outcome reporting to be unclear. For preventive healthcare, ATCS (ATCS Plus, IVR, unidirectional) probably increase immunisation uptake in children (risk ratio (RR) 1.25, 95% confidence interval (CI) 1.18 to 1.32; 5 studies, N = 10,454; moderate certainty) and to a lesser extent in adolescents (RR 1.06, 95% CI 1.02 to 1.11; 2 studies, N = 5725; moderate certainty). The effects of ATCS in adults are unclear (RR 2.18, 95% CI 0.53 to 9.02; 2 studies, N = 1743; very low certainty). For screening, multimodal ATCS increase uptake of screening for breast cancer (RR 2.17, 95% CI 1.55 to 3.04; 2 studies, N = 462; high certainty) and colorectal cancer (CRC) (RR 2.19, 95% CI 1.88 to 2.55; 3 studies, N = 1013; high certainty) versus usual care. It may also increase osteoporosis screening. ATCS Plus interventions probably slightly increase cervical cancer screening (moderate certainty), but effects on osteoporosis screening are uncertain. IVR systems probably increase CRC screening at 6 months (RR 1.36, 95% CI 1.25 to 1.48; 2 studies, N = 16,915; moderate certainty) but not at 9 to 12 months, with probably little or no effect of IVR (RR 1.05, 95% CI 0.99, 1.11; 2 studies, 2599 participants; moderate certainty) or unidirectional ATCS on breast cancer screening. Appointment reminders delivered through IVR or unidirectional ATCS may improve attendance rates compared with no calls (low certainty). For long-term management, medication or laboratory test adherence provided the most general evidence across conditions (25 studies, data not combined). Multimodal ATCS versus usual care showed conflicting effects (positive and uncertain) on medication adherence. ATCS Plus probably slightly (versus control; moderate certainty) or probably (versus usual care; moderate certainty) improves medication adherence but may have little effect on adherence to tests (versus control). IVR probably slightly improves medication adherence versus control (moderate certainty). Compared with usual care, IVR probably improves test adherence and slightly increases medication adherence up to six months but has little or no effect at longer time points (moderate certainty). Unidirectional ATCS, compared with control, may have little effect or slightly improve medication adherence (low certainty). The evidence suggested little or no consistent effect of any ATCS type on clinical outcomes (blood pressure control, blood lipids, asthma control, therapeutic coverage) related to adherence, but only a small number of studies contributed clinical outcome data. The above results focus on areas with the most general findings across conditions. In condition-specific areas, the effects of ATCS varied, including by the type of ATCS intervention in use. Multimodal ATCS probably decrease both cancer pain and chronic pain as well as depression (moderate certainty), but other ATCS types were less effective. Depending on the type of intervention, ATCS may have small effects on outcomes for physical activity, weight management, alcohol consumption, and diabetes mellitus. ATCS have little or no effect on outcomes related to heart failure, hypertension, mental health or smoking cessation, and there is insufficient evidence to determine their effects for preventing alcohol/ substance misuse or managing illicit drug addiction, asthma, chronic obstructive pulmonary disease, HIV/AIDS, hypercholesterolaemia, obstructive sleep apnoea, spinal cord dysfunction or psychological stress in carers. Only four trials (3%) reported adverse events, and it was unclear whether these were related to the intervention

Disparities in Glycemic Control Among Hispanic Adults With Diabetes

Background/Aims: Poor glycemic control is associated with increased morbidity and mortality for adults with diabetes mellitus (DM). Little research has examined disparities in glycemic control among Hispanics with DM compared to whites. The objective of this work was to determine: 1) whether disparities in glycemic control exist among Hispanics versus whites; and 2) whether demographics, socioeconomic status, disease characteristics, health care utilization (primary care, specialty care, care management services) and treatment characteristics (oral hypoglycemic medications, insulin use) explain differences in glycemic control. Methods: Using an observational study design, we studied 29,825 adults on the Kaiser Permanente Northwest DM registry as of January 1, 2013, with a valid HbA1c test during calendar year 2013. Good glycemic control was defined as HbA1c 30 (vs. body mass index \u3c 30), Charlson comorbidity score (continuous), primary care utilization in 2013 (1+ visits vs. none), specialty care utilization (1+ visits vs. none), use of DM care management services (1+ services vs. none), use of oral hypoglycemic medications (1+ medications fills vs. none) and insulin use (any insulin use vs. none). Seven logistic models were constructed: model 1 (race/ethnicity), model 2 (model 1 + demographics), model 3 (model 2 + SES), model 4 (model 3 + disease characteristics), model 5 (model 4 + health care utilization) and model 6 (model 5 + treatment characteristics). Results: Hispanics were less likely to have good glycemic control in unadjusted models (odds ratio: 0.56, 95% confidence interval [CI]: 0.51–0.61; P\u3c0.0001). This point estimate remained consisted across all logistic models examined, even after adjusting for covariate measures (odds ratio: 0.69, 95% CI: 0.62–0.77; P\u3c0.0001). Discussion: Our findings suggest that disparities in glycemic control among Hispanics compared to whites remain even after adjusting for critical covariate measures. More work is needed to understand whether lifestyle choices and other factors explain differences and whether targeted interventions can reduce these disparities

Assessing the Association Between Exercise Status and Poor Glycemic Control

Background: Increased physical activity may be associated with greater glycemic control among adults with diabetes mellitus. However, the area is understudied. The objective of the study was to examine the independent association of exercise status with poor glycemic control, adjusting for patient-level covariates. Methods: We studied a population of Kaiser Permanente Northwest members with type 2 diabetes mellitus who were in: 1) good glycemic control (hemoglobin A1c [HbA1c] \u3c 8%; n = 15,891), or 2) poor glycemic control (HbA1c \u3e 9%; n = 3,709). Additional inclusion criteria included an HbA1c test between July 1, 2014, and June 30, 2015, age 18-plus at time of test, and continuous health plan coverage 12 months prior to HbA1c test. The primary independent was current physical activity status — whether an individual exercised 4 or more times per week (yes vs no) — and was assessed as closely as possible to the HbA1c test date. Multiple logistic regression was used to analyze the independent association of exercise status with poor glycemic control, adjusting for demographics, medication adherence, medical comorbidities, health care utilization, receipt of diabetes mellitus care management services and intensity of diabetes mellitus treatments. Results: Those who exercised 4 or more times per week were less likely to have poor glycemic control (odds ratio: 0.75; 95% confidence interval: 0.68–0.82; P \u3c 0.0001) compared with those who exercised 3 or fewer times per week. Conclusion: Increased exercise is independently associated with a lower likelihood of poor glycemic control among an adult population with type 2 diabetes mellitus. Because physical activity is a potentially modifiable factor, further studies are needed to evaluate whether interventions aimed at increasing physical activity result in subsequent gains in glycemic control

PS3-13: Patient Barriers to Mammography Identified During a Mammogram Reminder System

Background/Aims: Patient mammogram reminders are effective at raising screening rates, but patient screening barriers remain. We evaluated patient characteristics available in electronic medical records that might be associated with mammogram completion after a reminder program, and identified patient-reported barriers that could help to explain response differences

Performance of a quantitative fecal immunochemical test for detecting advanced colorectal neoplasia: a prospective cohort study

Abstract Background The fecal immunochemical test (FIT) is easier to use and more sensitive than the guaiac fecal occult blood test, but it is unclear how to optimize FIT performance. We compared the sensitivity and specificity for detecting advanced colorectal neoplasia between single-sample (1-FIT) and two-sample (2-FIT) FIT protocols at a range of hemoglobin concentration cutoffs for a positive test. Methods We recruited 2,761 average-risk men and women ages 49-75 referred for colonoscopy within a large nonprofit, group-model health maintenance organization (HMO), and asked them to complete two separate single-sample FITs. We generated receiver-operating characteristic (ROC) curves to compare sensitivity and specificity estimates for 1-FIT and 2-FIT protocols among those who completed both FIT kits and colonoscopy. We similarly compared sensitivity and specificity between hemoglobin concentration cutoffs for a single-sample FIT. Results Differences in sensitivity and specificity between the 1-FIT and 2-FIT protocols were not statistically significant at any of the pre-specified hemoglobin concentration cutoffs (10, 15, 20, 25, and 30 μg/g). There was a significant difference in test performance of the one-sample FIT between 50 ng/ml (10 μg/g) and each of the higher pre-specified cutoffs. Disease prevalence was low. Conclusions A two-sample FIT is not superior to a one-sample FIT in detection of advanced adenomas; the one-sample FIT at a hemoglobin concentration cutoff of 50 ng/ml (10 μg/g) is significantly more sensitive for advanced adenomas than at higher cutoffs. These findings apply to a population of younger, average-risk patients in a U.S. integrated care system with high rates of prior screening

PS2-21: The Return on Investment of a Laboratory Program to Decrease Adverse Events of ACE Inhibitors and ARBs

Introduction: The efficiency of patient safety interventions is not well studied, including laboratory monitoring for drug therapy. Although 30% of patients receiving inhibitors of rennin-angiotensin system (RAS i.e. ACEI and ARB medications) are not monitored according to guidelines, knowledge of the efficiency of decreasing adverse drug events through monitoring programs above usual care is lacking

Improved Therapeutic Monitoring With Several Interventions

Background Medication errors are frequently related to failure to appropriately select medications or adjust for laboratory parameters. Differences between guideline recommendations and actual frequency of therapeutic laboratory monitoring are substantial. This study evaluated interventions to improve laboratory monitoring at initiation of medication therapy. Methods This cluster-randomized trial compared 3 interventions to usual care for 10 medications in 15 primary care clinics in a health maintenance organization with an electronic medical record system. Eligible patients, identified from electronic databases, had not received recommended laboratory monitoring within 5 days after new dispensing of a study medication. Interventions were an electronic medical record reminder to the prescribing health care professional, an automated voice message to the patient, and a pharmacy team outreach to the patient. Primary outcome was completion of all recommended baseline laboratory monitoring. Results A total of 961 patients participated in the study. At 25 days, 95 (48.5%) of 196 patients in the electronic medical record reminder group, 177 (66.3%) of 267 in the automated voice message group, 214 (82.0%) of 261 in the pharmacy team outreach group, and 53 (22.4%) of 237 in the usual care group had completed all recommended baseline laboratory monitoring (P<.001). After adjustments, the hazard ratios for completing laboratory monitoring compared with usual care were 2.5 (95% confidence interval, 1.8-3.5) for electronic medical record reminder, 4.1 (95% confidence interval, 3.0-5.6) for automated voice message, and 6.7 (95% confidence interval, 4.9-9.0) for pharmacy team outreach. Conclusions All 3 interventions were effective in increasing laboratory monitoring when initiating new medications in primary care. Further work is necessary to determine if these interventions improve patient outcomes