Organizing diabetes care: identify, monitor, prioritize, intensify

Associations between Outpatient Laboratory Test Age and Healthcare Utilization in Type 2 Diabetes Care

Purpose

To examine the longitudinal relationship between the age or shelf-life of common type 2 diabetes laboratory tests for serum creatinine, cholesterol, and glycated hemoglobin A1c conducted in outpatient settings and subsequent inpatient hospitalizations and emergency department visits.

Methods

This study analyzes panel data from two healthcare delivery systems' electronic health records (EHR) for patients aged 18 years and older managing type 2 diabetes. We used EHR data to quantify the age of three laboratory tests: serum creatinine, cholesterol, and glycated hemoglobin A1c. Encounter data were used to determine the frequency of inpatient hospitalizations and emergency department visits. Negative binomial regressions with fixed effects were performed to compute marginal effects, levels of statistical significance, and 95% confidence intervals.

Results

The average age for serum creatinine laboratory tests was 1.51 months (95%CI: 1.49-1.53). We computed older average ages for hemoglobin A1c (mean:6.17 months; 95%CI: 6.11-6.23) and serum creatinine tests (mean: 8.73; 95%CI: 8.65-8.81). Older laboratory tests were associated with an increase in the total expected counts of subsequent inpatient hospitalizations (ME = 0.047; p < 0.001) and ED visits (ME = 0.034; p < 0.001).

Conclusion

Findings from this study indicate that older type 2 diabetes laboratory tests are associated with increases in the total expected count of subsequent inpatient hospitalizations and emergency department visits. Future research should examine the actionability of laboratory test values to determine associations with healthcare outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40200-023-01250-0.

Quantifying Electronic Health Record Data Quality in Telehealth and Office-Based Diabetes Care

OBJECTIVE

Data derived from the electronic health record (EHR) are commonly reused for quality improvement, clinical decision-making, and empirical research despite having data quality challenges. Research highlighting EHR data quality concerns has largely been examined and identified during traditional in-person visits. To understand variations in data quality among patients managing type 2 diabetes mellitus (T2DM) with and without a history of telehealth visits, we examined three EHR data quality dimensions: timeliness, completeness, and information density.

METHODS

We used EHR data (2016-2021) from a local enterprise data warehouse to quantify timeliness, completeness, and information density for diagnostic and laboratory test data. Means and chi-squared significance tests were computed to compare data quality dimensions between patients with and without a history of telehealth use.

RESULTS

Mean timeliness or T2DM measurement age for the study sample was 77.8 days (95% confidence interval [CI], 39.6-116.4). Mean completeness for the sample was 0.891 (95% CI, 0.868-0.914). The mean information density score was 0.787 (95% CI, 0.747-0.827). EHR data for patients managing T2DM with a history of telehealth use were timelier (73.3 vs. 79.8 days), and measurements were more uniform across visits (0.795 vs. 0.784) based on information density scores, compared with patients with no history of telehealth use.

CONCLUSION

Overall, EHR data for patients managing T2DM with a history of telehealth visits were generally timelier and measurements were more uniform across visits than for patients with no history of telehealth visits. Chronic disease care relies on comprehensive patient data collected via hybrid care delivery models and includes important domains for continued data quality assessments prior to secondary reuse purposes.

Lessons from implementing a combined workflow-informatics system for diabetes management

Shortcomings surrounding the care of patients with diabetes have been attributed largely to a fragmented, disorganized, and duplicative health care system that focuses more on acute conditions and complications than on managing chronic disease. To address these shortcomings, we developed a diabetes registry population management application to change the way our staff manages patients with diabetes. Use of this new application has helped us coordinate the responsibilities for intervening and monitoring patients in the registry among different users. Our experiences using this combined workflow-informatics intervention system suggest that integrating a chronic disease registry into clinical workflow for the treatment of chronic conditions creates a useful and efficient tool for managing disease.

Critical features for a successful implementation of a diabetes registry

Numerous studies indicate a gap between evidence-based recommendations for care and clinical outcomes. Barriers reported by physicians include inefficiencies in data gathering. Information technology provides a potent solution to this problem. Diabetes registries are increasingly being utilized as a critical feature for population-based disease management. As an increasing number of organizations realize the need to choose among a plethora of different diabetes registry systems, we seek to identify important features in successful programs and address several of the critical features essential to an effective diabetes registry. A diabetes registry can be used to target high-risk patients, send reminders to patients, and provide physicians with feedback on their care, all important aspects of an optimal diabetes registry that can lead to significant improvements in clinical outcomes. As the healthcare system moves toward wider adoption of diabetes registries, key features of diabetes registry use are discussed.

Diabetes care credit system: a model for comprehensive and optimal diabetes care

OBJECTIVE

To introduce a "credit system," which empowers patients with diabetes to share in the implementation of guidelines with the health-care provider and provides credits for achievement of health variables considered essential for optimal diabetes care.

METHODS

We describe the credit system and the points offered for achievement of variables that assess lifestyle, glycohemoglobin, blood pressure, lipid values, measurement of urinary albumin, annual physical examination, dilated eye examination, foot examination, and electrocardiogram, use of antiplatelet therapy, and use of angiotensin receptor blocker (ARB) or angiotensin-converting enzyme (ACE) inhibitor therapy. The patient's knowledge of diabetes and nutrition was also assessed. Patients with diabetes mellitus were enrolled in the credit program during their scheduled office visits. Results were compared with those in a control group.

RESULTS

The study enrolled 613 patients (303 male and 310 female patients), with a mean age of 59.91 +/- 13.57 years (range, 16 to 90). The control group consisted of 119 patients with diabetes (63 men and 56 women; mean age, 55.94 +/- 14.83 years) not participating in the credit system. One year after initiation of the program, analysis of the study group demonstrated high rates of implementation of guidelines: complete physical examination, 94%; foot examination, 89%; dilated eye examination, 77%; blood pressure less than 135/85 mm Hg, 78%; hemoglobin A1c less than 6%, 7%, and 8%, 26%, 58%, and 82%, respectively; serum triglycerides less than 200 mg/dL, 61%; low-density lipoprotein cholesterol less than 100 mg/dL, 49%; high-density lipoprotein cholesterol more than 35 mg/dL, 76%; use of antiplatelet therapy, 60%; quantitative urinary albumin screening, 74%; and use of ACE inhibitors or ARB, 44%. These rates of achievement of established variables for hemoglobin A1c, serum lipids, blood pressure, antiplatelet therapy, and measurement of urinary albumin were significantly higher than those in the control subjects.

CONCLUSION

The described credit system demonstrates an effective and practical means to implement and achieve the necessary guidelines and target variables that are intended to lead to optimal diabetes and cardiovascular outcomes.

Is patient readiness to change a predictor of improved glycemic control?

OBJECTIVE

To test the hypothesis that patient readiness to change (RTC) predicts future changes in glycemic control in adults with diabetes.

RESEARCH DESIGN AND METHODS

We linked survey data with HbA1c data for a stratified random sample of consenting adults with diabetes. Change in HbA1c from baseline to the 1-year follow-up was computed and used as a dependent variable. Linear regression models assessed RTC and other patient variables as predictors of HbA1c change.

RESULTS

Among 617 patients with baseline HbA1c > or = 7% and complete data for analysis, RTC predicted subsequent improvement in HbA1c for those with higher physical functioning (interaction t = -2.45, P < 0.05). Other factors that predicted HbA1c improvement in multivariate linear regression models included higher self-reported medication adherence (t = -4.41, P < 0.01), higher baseline HbA1c (t = -15.08, P < 0.01), and older age (t = -2.61, P < 0.01).

CONCLUSIONS

Diabetes RTC independently predicts change in HbA1c for patients with high but not for patients with low functional health status. Customized use of RTC assessment may have potential to improve care.

Quality of recording of data from patients with type 2 diabetes is not a valid indicator of quality of care. A cross-sectional study

BACKGROUND

The quality of recording of clinical data in diabetes care in general practices is very variable. It has been suggested that better recording leads to improved glycaemic control.

OBJECTIVES

The purpose of this study was to assess the completeness of recording by GPs of data from type 2 diabetes patients; to compare recorded and missing data; and to investigate the association between completeness and glycaemic control.

METHODS

A cross-sectional survey was carried out in 52 general practices. Medical records were scrutinized for the presence of 11 variables. Examining patients through an active approach completed incomplete records. We compared recorded and unrecorded items. Completeness of recording was determined at both patient and practice levels.

RESULTS

Fifty-two general practices with 1641 type 2 diabetes patients cared for by the GP participated. The frequency of absence of any particular item ranged from 20 to 70%. Weight, systolic blood pressure and HbA(1c) were slightly lower in patients with those items missing on their files, and more such patients were non-smokers (P < 0.05). The percentage of patients with unrecorded variables that exceeded target values ranged from 39 to 75. Neither at practice level nor at patient level was any association between the completeness of the data recording and HbA(1c) found.

CONCLUSION

Records often were incomplete, which hampers a systematic approach to care of diabetic patients. However, the lack of association between completeness of data recording and control of glycaemia indicates that improved recording is not a valid indicator of good quality of care.