Factors Associated With Intensification of Oral Diabetes Medications in Primary Care Provider-Patient Dyads: A Cohort Study

Bolen, Shari; Bricker, Eric; Samuels, T. Alafia; Yeh, Hsin Chieh; Marinopoulos, Spyridon S; McGuire, Maura; Abuid, Marcela; Brancati, Frederick L.

doi:10.2337/dc08-1297

Cited by 48 publications

(42 citation statements)

References 20 publications

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“…Many factors contribute to this problem, including competing demands at the time of the visit 3 and medication nonadherence. 4 Rates of treatment intensifi cation when patients are not at goal, however, hover around 70% to 80%, 5 and several studies have linked higher rates of treatment intensifi cation by a primary care physician to better rates of hypertension, lipid, or glucose control in that primary care physician's patients. 6 In theory, treatment intensifi cation and control of hemoglobin A 1c , blood pressure, and lipid levels in patients with diabetes mellitus could be improved by providing patient-specifi c and drug-specifi c clinical decision support at the time of a clinical encounter.…”

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confidence: 99%

Impact of Electronic Health Record Clinical Decision Support on Diabetes Care: A Randomized Trial

O’Connor¹,

Sperl-Hillen²,

Rush³

et al. 2011

The Annals of Family Medicine

223

186

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PURPOSE We wanted to assess the impact of an electronic health record-based diabetes clinical decision support system on control of hemoglobin A 1c (glycated hemoglobin), blood pressure, and low-density lipoprotein (LDL) cholesterol levels in adults with diabetes. METHODSWe conducted a clinic-randomized trial conducted from October 2006 to May 2007 in Minnesota. Included were 11 clinics with 41 consenting primary care physicians and the physicians' 2,556 patients with diabetes. Patients were randomized either to receive or not to receive an electronic health record (EHR)-based clinical decision support system designed to improve care for those patients whose hemoglobin A 1c , blood pressure, or LDL cholesterol levels were higher than goal at any offi ce visit. Analysis used general and generalized linear mixed models with repeated time measurements to accommodate the nested data structure. RESULTSThe intervention group physicians used the EHR-based decision support system at 62.6% of all offi ce visits made by adults with diabetes. The intervention group diabetes patients had signifi cantly better hemoglobin A 1c (intervention effect -0.26%; 95% confi dence interval, -0.06% to -0.47%; P = .01), and better maintenance of systolic blood pressure control (80.2% vs 75.1%, P = .03) and borderline better maintenance of diastolic blood pressure control (85.6% vs 81.7%, P = .07), but not improved low-density lipoprotein cholesterol levels (P = .62) than patients of physicians randomized to the control arm of the study. Among intervention group physicians, 94% were satisfi ed or very satisfi ed with the intervention, and moderate use of the support system persisted for more than 1 year after feedback and incentives to encourage its use were discontinued.CONCLUSIONS EHR-based diabetes clinical decision support signifi cantly improved glucose control and some aspects of blood pressure control in adults with type 2 diabetes. INTRODUCTIOND espite recent improvement trends in the United States, in 2008 less than 20% of patients with diabetes concurrently reach evidence-based goals for hemoglobin A 1c (glycated hemoglobin), systolic and diastolic blood pressure, and low-density lipoprotein (LDL) cholesterol levels.1,2 Care is unsatisfactory in both subspecialty and primary care settings, but because more than 80% of diabetes care is delivered by primary care physicians, effective strategies to improve diabetes care in primary care settings are urgently needed.Among the major barriers to better diabetes care is lack of timely intensifi cation of pharmacotherapy in patients who have not achieved recommended clinical goals. Many factors contribute to this problem, including competing demands at the time of the visit 3 and medication In theory, treatment intensifi cation and control of hemoglobin A 1c , blood pressure, and lipid levels in patients with diabetes mellitus could be improved by providing patient-specifi c and drug-specifi c clinical decision support at the time of a clinical encounter. Electronic health recor...

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mentioning

confidence: 99%

Impact of Electronic Health Record Clinical Decision Support on Diabetes Care: A Randomized Trial

O’Connor¹,

Sperl-Hillen²,

Rush³

et al. 2011

The Annals of Family Medicine

223

186

View full text Add to dashboard Cite

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“…38 While findings continually point to delays in treatment intensification, 39-43 findings of differences in delay length by race have been inconsistent. [39][40][41][42][43] On the other hand, studies of medication adherence have consistently found black patients to be less adherent to prescribed medications. [22][23][24] Our understanding of the impact of medication adherence and treatment intensification to observed racial differences in risk factor control among patients with diabetes remains limited.…”

Section: Discussionmentioning

confidence: 99%

Medication Adherence Does Not Explain Black-White Differences in Cardiometabolic Risk Factor Control among Insured Patients with Diabetes

et al. 2015

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BACKGROUND: Among patients with diabetes, racial differences in cardiometabolic risk factor control are common. The extent to which differences in medication adherence contribute to such disparities is not known. We examined whether medication adherence, controlling for treatment intensification, could explain differences in risk factor control between black and white patients with diabetes. METHODS: We identified three cohorts of black and white patients treated with oral medications and who had poor risk factor control at baseline (2009): those with glycated hemoglobin (HbA1c) >8 % (n=37,873), low-density lipoprotein cholesterol (LDL-C) >100 mg/dl (n=27,954), and systolic blood pressure (SBP) >130 mm Hg (n=63,641). Subjects included insured adults with diabetes who were receiving care in one of nine U.S. integrated health systems comprising the SUrveillance, PREvention, and ManagEment of Diabetes Mellitus (SUPREME-DM) consortium. Baseline and follow-up risk factor control, sociodemographic, and clinical characteristics were obtained from electronic health records. Pharmacydispensing data were used to estimate medication adherence (i.e., medication refill adherence [MRA]) and treatment intensification (i.e., dose increase or addition of new medication class) between baseline and follow-up. County-level income and educational attainment were estimated via geocoding. Logistic regression models were used to test the association between race and follow-up risk factor control. Models were specified with and without medication adherence to evaluate its role as a mediator. RESULTS: We observed poorer medication adherence among black patients than white patients (p < 0.01): 50.6 % of blacks versus 39.7 % of whites were not highly adherent (i.e., MRA <80 %) to HbA1c oral medication(s); 58.4 % of blacks and 46.7 % of whites were not highly adherent to lipid medication(s); and 33.4 % of blacks and 23.7 % of whites were not highly adherent to BP medication(s). Across all cardiometabolic risk factors, blacks were significantly less likely to achieve control (p<0.01): 41.5 % of blacks and 45.8 % of whites achieved HbA1c <8 %; 52.6 % of blacks and 60.8 % of whites achieved LDL-C <100; and 45.7 % of blacks and 53.6 % of whites achieved SBP <130. Adjusting for medication adherence/treatment intensification did not alter these patterns or model fit statistics. CONCLUSIONS: Medication adherence failed to explain observed racial differences in the achievement of HbA1c, LDL-C, and SBP control among insured patients with diabetes.

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“…For now, it seems that the findings such as those from the Tsukuba Kawai Diabetes Registry study might at least provide another reason for minimising the delays known to occur before glycaemic treatments in type 2 diabetic patients are intensified [16]. Rather than just delaying an improvement in glycaemia, any delay in intensification might also worsen HbA 1c variability, compounding the risk of complications when another agent is eventually introduced.…”

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confidence: 99%

The rise and fall of HbA1c as a risk marker for diabetes complications

Kilpatrick

2012

Diabetologia

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Factors Associated With Intensification of Oral Diabetes Medications in Primary Care Provider-Patient Dyads: A Cohort Study

Cited by 48 publications

References 20 publications

Impact of Electronic Health Record Clinical Decision Support on Diabetes Care: A Randomized Trial

Impact of Electronic Health Record Clinical Decision Support on Diabetes Care: A Randomized Trial

Medication Adherence Does Not Explain Black-White Differences in Cardiometabolic Risk Factor Control among Insured Patients with Diabetes

The rise and fall of HbA1c as a risk marker for diabetes complications

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