Dataset Information

A quality improvement project to increase compliance with diabetes measures in an academic outpatient setting.

ABSTRACT:

Background

American Diabetes Association (ADA) sets annual guidelines on preventative measures that aim to delay the onset of severe diabetes mellitus complications. Compared to private internal medicine clinics, resident clinics provide suboptimal diabetic preventative care as evidenced by decreased compliance with ADA guidelines. The purpose of our study is to improve diabetic care in resident clinics through quality improvement (QI) projects, with A1C value as primary outcome and other ADA guidelines as secondary outcomes.

Methods

Our resident clinic at Beaumont Hospital, Royal Oak consists of 76 residents divided in 8 teams. In November 2016, baseline data on ADA guideline measures was obtained on 538 patients with diabetes mellitus. A root cause analysis was conducted. 5 teams developed a QI intervention plan to improve their diabetes care and 3 teams served as comparisons without intervention plans. In November 2017, post-intervention data was collected.

Results

Baseline characteristics demonstrate mean age of intervention groups at 60.9 years and of comparison groups at 58.9 years. The change in A1C value from baseline to post-intervention was + 0.09 vs. + 0.322 in the intervention and comparison groups respectively (p = 0.174). As a group, the changes in secondary outcome measures were as follows: eye examinations (+ 5% in intervention vs. -7% in comparison, p < 0.01), foot examinations (+ 13% vs. + 5%, p = 0.09), lipid panel testing (+ 7% vs. -5%, p < 0.01), micro-albumin/creatinine ratio testing (+ 4% vs. + 1%, p = 0.03), and A1C testing (+8% vs. + 5%, p = 0.24).

Conclusions

While the QI project did not improve A1C value, it did have significant improvement in several secondary outcomes within intervention groups. One resident team implemented an intervention involving protected half-day blocks to identify overdue examinations and consequently had the largest improvements, thus serving as a potential intervention to further study. Given our study results, we believe that QI interventions improve preventative care for patients with diabetes in resident clinics.

SUBMITTER: Edupuganti S

PROVIDER: S-EPMC6651972 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Obstructive sleep apnoea (OSA) is more prevalent in patients with hypertension (HTN), and associated morbidities include stroke, heart failure and premature death. In the Internal Medicine Clinic (IMC), over 70% of the patients had a diagnosis of HTN and obesity. We identified a lack of OSA screening in patients with HTN. The aim of this quality improvement (QI) was to increase OSA diagnosis to 5% from the baseline rate of less than 1% in patients with HTN between the ages of 18 and 75 years over 6 months at IMC. We used the Plan-Do-Study-Act (PDSA) method. The QI team performed root cause analysis to identify materials/methods, provider and patient-related barriers. PDSA cycle included: (1) integration of customised workflow of loud Snoring, Tiredness, Observed apnea, high blood Pressure (STOP)-Body mass index (BMI), Age, Neck circumference, and Gender (BANG) OSA screening tool in the electronic health record (EHR); (2) physician education of OSA and EHR workflow; and (3) completion of STOP survey by patients, which was facilitated by nursing staff. The outcome measure was the percentage of OSA diagnosis in patients with HTN. The process measures included the percentage of patients with HTN screened for OSA and the increase in sleep study referrals in hypertensive patients with STOP-BANG score of ?3. Increase in patient wait time and cost of sleep study were the balance measures. Data analysis was performed using weekly statistical process control chart. The average increase in OSA screening rate using the STOP-BANG tool was 3.88%. The significant variation seen in relation to PDSA cycles was not sustainable. 32% of patients scored ?3 on the STOP-BANG tool, and 10.4% had a confirmed diagnosis of OSA. STOP-BANG tool integration in the EHR and a team approach did not result in a sustainable increase in OSA screening. OSA diagnosis was increased to 3.3% in IMC patient population within the 6-month period. The team identified multiple barriers to screening and diagnosis of OSA in the IMC.