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ABSTRACT
Objectives: To compare the frequency of self-reported gaps in care coordination and self-reported preventable adverse events among adults with vs without diabetes.
Study Design: Cross-sectional analysis of REasons for Geographic And Racial Differences in Stroke (REGARDS) study participants 65 years and older who completed a survey on health care experiences in 2017-2018 (N = 5634).
Methods: We analyzed the association of diabetes with self-reported gaps in care coordination and with preventable adverse events. Gaps in care coordination were assessed using 8 validated questions. Four self-reported adverse events were studied (drug-drug interactions, repeat medical tests, emergency department visits, and hospitalizations). Respondents were asked if they thought these events could have been prevented with better communication among providers.
Results: Overall, 1724 (30.6%) participants had diabetes. Among participants with and without diabetes, 39.3% and 40.7%, respectively, reported any gap in care coordination. The adjusted prevalence ratio (aPR) for any gap in care coordination for participants with vs without diabetes was 0.97 (95% CI, 0.89-1.06). Any preventable adverse event was reported by 12.9% and 8.7% of participants with and without diabetes, respectively. The aPR for any preventable adverse event for participants with vs without diabetes was 1.22 (95% CI, 1.00-1.49). Among participants with and without diabetes, the aPRs for any preventable adverse event associated with any gap in care coordination were 1.53 (95% CI, 1.15-2.04) and 1.50 (95% CI, 1.21-1.88), respectively (P comparing aPRs = .922).
Conclusions: Interventions to improve quality of care for patients with diabetes could incorporate patient-reported gaps in care coordination to aid in preventing adverse events.
Am J Manag Care. 2023;29(6):In Press
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Takeaway Points
In the current analysis of 5634 adults 65 years and older who completed a survey on their experiences with health care in 2017-2018:
- Gaps in care coordination were common among adults with and without diabetes.
- Participants with diabetes were more likely than those without diabetes to report an adverse event they felt would have been preventable with better care coordination.
- Reporting a gap in care coordination was associated with a higher likelihood of reporting a preventable adverse event among participants with and without diabetes.
- Interventions to improve quality of care for diabetes could identify and address patient-reported gaps in care coordination to prevent adverse events.
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Given their high burden of diabetes-related organ damage and chronic comorbid conditions,1-3 patients with diabetes may receive care from health care providers in different specialties.1,4,5 Although receiving care from multiple health care providers may be clinically appropriate, patients’ health information is not always shared among providers.6,7 When providers do not communicate with each other, they may not coordinate evaluations or treatments, which could result in duplicate tests, drug-drug interactions, excess procedures, and avoidable emergency department (ED) visits and hospitalizations.8-11
Patients are aware of gaps that may occur in their care coordination10,12,13 and of adverse health care–related events that they perceive could have been prevented with better care coordination, herein referred to as preventable adverse events.10 However, little is known about patient-reported gaps in care coordination or preventable adverse events among patients with diabetes. It is unclear whether reported gaps in care coordination are associated with preventable adverse events among patients with diabetes. If patients with diabetes are more likely to report gaps in care coordination and preventable adverse events compared with those without diabetes, this would represent undesirable care processes that are potentially modifiable.
The aim of this study was to determine whether adults with diabetes are more likely to report gaps in care coordination or, separately, preventable adverse events vs those without diabetes. We also determined whether self-reported gaps in care coordination are associated with self-reported preventable adverse events among adults with diabetes. To accomplish these aims, we analyzed data from the REasons for Geographic And Racial Differences in Stroke (REGARDS) study.
METHODS
Study Population
REGARDS is a population-based cohort study that recruited 30,239 Black and White adults 45 years and older from the 48 contiguous US states and the District of Columbia between January 2003 and October 2007. Black adults and adults residing in the Southeastern United States were oversampled by design.14 Participants completed a computer-assisted telephone interview (CATI) and an in-home study visit at baseline and during 2013-2016, and they completed follow-up phone interviews every 6 months following baseline to identify potential stroke and myocardial infarction (MI) events that were adjudicated by experts based on medical records. Between August 2017 and November 2018, during one of their follow-up interviews, REGARDS study participants were invited to complete a survey on experiences with health care. The institutional review boards at all participating institutions approved the REGARDS study, and all participants provided written informed consent.
We analyzed data from REGARDS study participants who were active in the cohort at the time the survey on experiences with health care was administered and who agreed to complete the survey (eAppendix Figure 1 [eAppendix available at ajmc.com]). We restricted the analysis to participants who were 65 years or older at the time of the survey, who reported having 2 or more health care visits with 2 or more health care providers in the 12 months prior to the survey (because those with only 1 visit to 1 provider were not at risk for problems with care coordination), who reported having a regular health care provider, and who reported seeing that provider in the 6 months prior to the survey. We excluded participants who did not complete the second CATI and in-home study visit and who were missing data on self-reported diabetes or self-reported use of glucose-lowering medication. Lastly, among participants who did not meet our definition of diabetes as defined in the next section, we excluded participants who were missing data on blood glucose levels, whose fasting blood glucose was 126 mg/dL or greater, or whose nonfasting blood glucose was 200 mg/dL or greater. These participants were excluded because they could have undiagnosed diabetes and may not have been receiving the same care for the condition as participants whose diabetes was diagnosed. In total, 5634 participants were included in the current analysis.
Diabetes
Diabetes was defined by a self-reported diagnosis of diabetes, self-reported use of glucose-lowering medication, or identification of a glucose-lowering medication on a medication inventory conducted at the second REGARDS in-home study visit. Participants taking metformin without other glucose-lowering medication who did not report a prior diagnosis of diabetes were not considered to have diabetes because these participants could have been taking metformin for prediabetes or weight loss.15,16 Participants not meeting this definition were considered to not have diabetes.
Gaps in Care Coordination and Preventable Adverse Events
We analyzed 7 gaps in care coordination (eAppendix Table 1) and 4 preventable adverse events (eAppendix Table 2) using data from the survey on experiences with health care.10 The 7 gaps in care coordination were defined based on 8 questions. Six of the questions assessed participants’ perceptions of the coordination of their care in the past 6 months (eg, “In the last 6 months, when you visited your personal doctor for a scheduled appointment, how often did he or she have your medical records or other information about your care?” [never, sometimes, usually, or always]). Two questions assessed participants’ overall perception of communication among their health care providers (eg, “In general, do you think the doctors who you see communicate with each other about your care?” [yes, no, or I don’t know]). The 4 preventable adverse events were (1) a medical test that was repeated because the results of the first test, conducted previously, were not available; (2) a drug-drug interaction caused by doctors prescribing medications that “did not go well together”; (3) an ED visit the participant felt would have been preventable with better care coordination; and (4) a hospital admission the participant felt would have been preventable with better care coordination.
Potential Confounders
We analyzed data on potential confounders such as age, annual household income, body mass index (BMI), hypertension, dyslipidemia, chronic kidney disease (CKD), atrial fibrillation, and peripheral artery disease, which were assessed at the second CATI and second in-home study visit. We also analyzed data on sex, race, educational attainment, and region of residence, which were assessed at the REGARDS study baseline. History of MI and stroke were defined using data from the baseline and second CATIs and study visits supplemented by adjudicated outcome events. We analyzed data on the number of ambulatory visits and ambulatory providers that participants reported having in the 12 months prior to completing their survey on experiences with health care. Among participants with diabetes, we also analyzed data on the duration of diabetes and insulin use, utilizing information collected at the second CATI and in-home study visit. Finally, for an explanatory analysis, we analyzed data on the number of prescription medications taken in the 2 weeks prior to each participant’s second in-home study visit. eAppendix Table 3 shows the definitions of these variables. eAppendix Figure 2 shows a schematic of the study design.
Statistical Analysis
We calculated summary statistics for participant characteristics and the proportion of participants who reported gaps in care coordination and preventable adverse events, overall and separately for those with and without diabetes. Three Poisson regression models with robust variance estimators were used to calculate prevalence ratios (PRs) and 95% CIs for reporting at least 1 gap in care coordination among participants with vs without diabetes. Model 1 was unadjusted. Model 2 included adjustment for age, sex, race, educational attainment, annual household income, and region of residence. Model 3 included adjustment for all variables in model 2 and the following clinical variables: BMI, hypertension, dyslipidemia, CKD, history of MI, history of stroke, atrial fibrillation, and peripheral artery disease. We also calculated the count of gaps in care coordination reported by participants. The distribution of the count of gaps was overdispersed with a large number of zero values, so we used marginalized zero-inflated Poisson regression models with adjustment as described above to calculate the ratio of the mean count among participants with vs without diabetes.17
We calculated PRs and 95% CIs for reporting at least 1 preventable adverse event associated with diabetes using 3 Poisson regression models with robust variance estimators and adjustment for potential confounders as described above. Adults with diabetes may take more prescription medications than those without diabetes. In an explanatory model, we calculated the PR and 95% CI for reporting at least 1 preventable adverse event associated with diabetes, including adjustment for the potential confounders in model 3 and number of prescription medications being taken. We considered the number of prescription medications to be a potential variable in the causal pathway because one mechanism by which gaps in care coordination may cause harm is through lack of communication about prescribing, which can lead to drug-drug interactions.18,19 Few participants had more than 1 preventable adverse event, so we did not compare the count of adverse events among those with vs without diabetes.
Among participants with and without diabetes, we calculated the PR and 95% CI for reporting at least 1 preventable adverse event associated with reporting at least 1 vs 0 gaps in care coordination. The models included adjustment for covariates as described above, a main effect for diabetes, and an interaction term between diabetes and gaps in care coordination, which was used to assess effect modification by diabetes status. In exploratory analyses, we compared the report of gaps in care coordination and preventable adverse events, and the association between gaps in care coordination and preventable adverse events among participants with diabetes using vs not using insulin.
RESULTS
Participant Characteristics
The proportions of participants who were Black, who had an annual household income less than $25,000, and who had comorbid conditions were higher in those with vs without diabetes, whereas the proportion who were college graduates was lower in participants with diabetes (Table 1). Compared with participants without diabetes, those with diabetes were taking more prescription medications. Participants with diabetes reported a higher number of health care providers and health care visits in the 12 months preceding the survey on experiences with health care compared with their counterparts without diabetes. Among participants with diabetes, the median duration of diabetes was 11 years and 28% were using insulin.
Gaps in Care Coordination
eAppendix Table 4 shows the frequency of each of the 7 gaps in care coordination, overall and in those with and without diabetes, separately. Among participants with diabetes, 39.3% reported at least 1 gap in care coordination compared with 40.7% in those without diabetes (Table 2). In the fully adjusted model, diabetes was not associated with reporting at least 1 gap in care coordination (adjusted PR [aPR], 0.97; 95% CI, 0.89-1.06). Diabetes was also not associated with the number of gaps in care coordination (ratio, 0.93; 95% CI, 0.84-1.03) (eAppendix Table 5).
Preventable Adverse Events
Participants with diabetes were more likely to report a drug-drug interaction, any ED visit, and any hospitalization vs those without diabetes (eAppendix Table 6). The proportion of participants who reported at least 1 preventable adverse event was 12.9% among those with diabetes and 8.7% in those without diabetes (Table 3). After multivariable adjustment for all potential confounders (model 3), the prevalence of reporting at least 1 preventable adverse event was higher for participants with vs without diabetes (aPR, 1.22; 95% CI, 1.00-1.49). After further adjustment for number of prescription medications (explanatory model), the aPR for reporting at least 1 preventable adverse event in adults with vs without diabetes was 1.10 (95% CI, 0.89-1.35).
Gaps in Care Coordination and Preventable Adverse Events
Among participants with diabetes, the proportion who reported at least 1 preventable adverse event was higher among those with at least 1 vs 0 gaps in care coordination (Table 4). The proportion of participants without diabetes reporting at least 1 preventable adverse event was also higher among those reporting at least 1 vs 0 gaps in care coordination. After multivariable adjustment, the aPRs for reporting at least 1 preventable adverse event associated with reporting at least 1 gap in care coordination were 1.53 (95% CI, 1.15-2.04) among participants with diabetes and 1.50 (95% CI, 1.21-1.88) among those without diabetes (P comparing PRs = .922).
Insulin, Gaps in Care Coordination, and Preventable Adverse Events
Among participants with diabetes, there was no association between insulin use and reporting a gap in care coordination (aPR, 0.88; 95% CI, 0.73-1.05) (eAppendix Table 7) or reporting a preventable adverse event (aPR, 1.03; 95% CI, 0.72-1.46) (eAppendix Table 8). In fully adjusted models, the aPRs for reporting at least 1 preventable adverse event associated with at least 1 vs 0 gaps in care coordination were 2.65 (95% CI, 1.45-4.83) in participants using insulin and 1.41 (95% CI, 1.02-1.96) in those with diabetes not using insulin (P comparing PRs = .069) (eAppendix Table 9).
DISCUSSION
In the current national study of Black and White adults 65 years and older who received care from multiple health care providers, there was no difference in self-reported gaps in care coordination by diabetes status. However, compared with those without diabetes, participants with diabetes were more likely to report an adverse event that they perceived could have been prevented with better care coordination. This difference appears to be explained, at least in part, by participants with diabetes taking more prescription medications, a risk factor for drug-drug interactions, vs those without diabetes. Participants with diabetes who reported any vs no gap in care coordination were more likely to report any preventable adverse event. The association between reporting any gap in care coordination and reporting any preventable adverse event appeared to be stronger among participants with diabetes using vs not using insulin.
In the current study, 39% of patients with diabetes reported a gap in care coordination. This proportion is quite high considering that most of the questions used to assess gaps in care coordination were restricted to the past 6 months. Improving diabetes quality of care has been a major goal in health care for decades, but quality improvement efforts have largely focused on increasing patient engagement in recommended screening tests and controlling intermediate clinical outcomes such as blood glucose levels.20,21 Patients’ experiences of care coordination are not often considered in quality measures for diabetes.22 The high proportion of patients with diabetes who report problems with care coordination suggests a need for quality improvement efforts in this area of care.
The current study adds to the existing literature by showing that patients with diabetes are aware of experiencing adverse events and that they attribute many of those events to poor care coordination. The current study also suggests that participants with diabetes may be more likely to report a preventable adverse event compared with their counterparts without diabetes despite not experiencing more gaps in care coordination. Previous studies have shown that patients with diabetes take more medications23 and have higher risk for any hospitalization vs those without diabetes.24 Therefore, experiencing gaps in care coordination may be particularly hazardous to patients with diabetes. Consistently, the association of diabetes with higher report of preventable adverse events in the current analysis was attenuated and no longer statistically significant after adjustment for number of prescription medications being taken. This finding supports the inference that patients with diabetes taking multiple medications need particular monitoring for gaps in communication among prescribers.
Using data from the REGARDS study, Kern et al previously showed that adults who report a gap in care coordination are more likely to report preventable adverse events.10 The current study expands prior knowledge by showing that this association is present among adults with diabetes. The current study also suggests that the association between gaps in care coordination and preventable adverse events may be stronger among adults with diabetes taking vs not taking insulin. Patients using insulin have more diabetes-related complications than those with diabetes not using this medication.25 Experiencing gaps in care coordination may have a more deleterious effect on the occurrence of preventable adverse events in adults with diabetes using vs not using insulin, a hypothesis that needs to be confirmed in future studies.
Previous quality improvement efforts for adults with diabetes have typically selected patients based on their hemoglobin A1c level or a recent hospitalization, which is appropriate, but this strategy may miss some opportunities for improvement.26-28 A future intervention to improve care coordination for patients with diabetes might start with identifying those who perceive that their care is not well coordinated. Patient safety experts have shown that patients’ perceptions of their care often have merit.29 Therefore, addressing patients’ concerns may help prevent future adverse events.
Strengths and Limitations
The current study has several strengths. The analysis included a large, national sample of patients with and without diabetes. Gaps in care coordination were assessed using previously validated questions. The current study also has several limitations. Diabetes was defined, in part, by self-report, which may have resulted in some misclassification. Gaps in care coordination and preventable adverse events were both defined using self-report of events occurring over long periods (ie, 6 and 12 months). Thus, there is the potential for inaccurate recall. Additionally, some of the questions about gaps in care coordination referred to events occurring over the 6 months preceding the survey on experiences with health care, whereas questions about preventable adverse events included events occurring over the 12 preceding months. It is possible that preventable adverse events may have preceded the gaps in care coordination in some participants. Lastly, the outcomes in the current study were subjective. Participants may have understood the question about drug-drug interactions (ie, medications not going well together) differently from one another. In addition, whether participants’ ED visits and hospitalizations could have been prevented with better-coordinated care cannot be known for certain.
CONCLUSIONS
A high proportion of older adults with and without diabetes receiving care from multiple health care providers reported a problem with the coordination of their care. Adults with diabetes were more likely to report an adverse event that they attributed to poor care coordination compared with their counterparts without diabetes. The frequency of these problems remains quite high despite decades of work to improve the quality of care for patients with diabetes. Whereas previous interventions to improve diabetes care have typically identified patients for inclusion based on severity of illness or transitions in care, this work suggests that new interventions are needed, which would identify patients based on their experiences of care. Identifying and addressing patient-reported gaps in care coordination would be a novel strategy that may increase quality of care, increase patient satisfaction, and improve patient safety.
Acknowledgments
The authors thank the other investigators, the staff, and the participants of the REGARDS study for their valuable contributions. A full list of participating REGARDS investigators and institutions can be found at https://www.uab.edu/soph/regardsstudy/.
Author Affiliations: University of Alabama at Birmingham (CLC, OPA, PM, LDC), Birmingham, AL; Weill Cornell Medicine (MR, MMS, LMK), New York, NY; University of Mississippi Medical Center (APC), Jackson, MS.
Source of Funding: Funding for the survey on perceptions of care coordination was provided by the National Heart, Lung, and Blood Institute (NHLBI; R01 HL135199). This research project was also supported by 1R01 HL165452-01, funded by the NHLBI. The REasons for Geographic And Racial Differences in Stroke study is supported by cooperative agreement U01 NS041588 co-funded by the National Institute of Neurological Disorders and Stroke (NINDS) and the National Institute on Aging (NIA), National Institutes of Health, HHS. This content is solely the responsibility of the authors and does not necessarily represent the official views of the NINDS or the NIA. Representatives of the NINDS were involved in the review of the manuscript but were not directly involved in the collection, management, analysis, or interpretation of the data. Representatives from the NHLBI did not have any role in the design and conduct of the study; the collection, management, analysis, and interpretation of the data; or the preparation or approval of the manuscript.
Author Disclosures: Dr Safford is the founder of MedExplain Inc. Dr Colantonio receives grant funding from Amgen. Dr Kern is a consultant to Mathematica Inc. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.
Authorship Information: Concept and design (CLC, OPA, MR, MMS, PM, LDC, LMK); acquisition of data (MMS, LMK); analysis and interpretation of data (CLC, OPA, MMS, APC, PM, LDC, LMK); drafting of the manuscript (CLC, MR); critical revision of the manuscript for important intellectual content (CLC, OPA, MR, MMS, APC, PM, LDC, LMK); statistical analysis (CLC, OPA, APC); provision of patients or study materials (LMK); obtaining funding (MMS, LMK); administrative, technical, or logistic support (MMS, LDC); and supervision (MMS, LDC, LMK).
Address Correspondence to: Lisa M. Kern, MD, MPH, Department of Medicine, Weill Cornell Medicine, 420 E 70th St, Box 331, New York, NY 10021. Email: lmk2003@med.cornell.edu.
REFERENCES
1. American Diabetes Association. 4. Comprehensive medical evaluation and assessment of comorbidities: Standards of Medical Care in Diabetes—2021. Diabetes Care. 2021;44(suppl 1):S40-S52. doi:10.2337/dc21-S004
2. Iglay K, Hannachi H, Howie PJ, et al. Prevalence and co-prevalence of comorbidities among patients with type 2 diabetes mellitus. Curr Med Res Opin. 2016;32(7):1243-1252. doi:10.1185/03007995.2016.1168291
3. Lin PJ, Kent DM, Winn A, Cohen JT, Neumann PJ. Multiple chronic conditions in type 2 diabetes mellitus: prevalence and consequences. Am J Manag Care. 2015;21(1):e23-e34.
4. American Diabetes Association. 1. Improving care and promoting health in populations: Standards of Medical Care in Diabetes—2021. Diabetes Care. 2021;44(suppl 1):S7-S14. doi:10.2337/dc21-S001
5. American Diabetes Association. 11. Microvascular complications and foot care: Standards of Medical Care in Diabetes—2021. Diabetes Care. 2021;44(suppl 1):S151-S167. doi:10.2337/dc21-S011
6. O’Malley AS, Reschovsky JD. Referral and consultation communication between primary care and specialist physicians: finding common ground. Arch Intern Med. 2011;171(1):56-65. doi:10.1001/archinternmed.2010.480
7. Mehrotra A, Forrest CB, Lin CY. Dropping the baton: specialty referrals in the United States. Milbank Q. 2011;89(1):39-68. doi:10.1111/j.1468-0009.2011.00619.x
8. Kern LM, Seirup JK, Casalino LP, Safford MM. Healthcare fragmentation and the frequency of radiology and other diagnostic tests: a cross-sectional study. J Gen Intern Med. 2017;32(2):175-181. doi:10.1007/s11606-016-3883-z
9. Kern LM, Seirup JK, Rajan M, Jawahar R, Stuard SS. Fragmented ambulatory care and subsequent healthcare utilization among Medicare beneficiaries. Am J Manag Care. 2018;24(9):e278-e284.
10. Kern LM, Reshetnyak E, Colantonio LD, et al. Association between patients’ self-reported gaps in care coordination and preventable adverse outcomes: a cross-sectional survey. J Gen Intern Med. 2020;35(12):3517-3524. doi:10.1007/s11606-020-06047-y
11. Kern LM, Ringel JB, Rajan M, et al. Ambulatory care fragmentation and subsequent hospitalization: evidence from the REGARDS Study. Med Care. 2021;59(4):334-340. doi:10.1097/mlr.0000000000001470
12. Wang MC, Mosen D, Shuster E, Bellows J. Association of patient-reported care coordination with patient satisfaction. J Ambul Care Manage. 2015;38(1):69-76. doi:10.1097/jac.0000000000000021
13. O’Malley AS, Cunningham PJ. Patient experiences with coordination of care: the benefit of continuity and primary care physician as referral source. J Gen Intern Med. 2009;24(2):170-177. doi:10.1007/s11606-008-0885-5
14. Howard VJ, Cushman M, Pulley L, et al. The REasons for Geographic And Racial Differences in Stroke Study: objectives and design. Neuroepidemiology. 2005;25(3):135-143. doi:10.1159/000086678
15. Crandall JP, Knowler WC, Kahn SE, et al; Diabetes Prevention Program Research Group. The prevention of type 2 diabetes. Nat Clin Pract Endocrinol Metab. 2008;4(7):382-393. doi:10.1038/ncpendmet0843
16. Diabetes Prevention Program Research Group. Long-term safety, tolerability, and weight loss associated with metformin in the Diabetes Prevention Program Outcomes Study. Diabetes Care. 2012;35(4):731-737. doi:10.2337/dc11-1299
17. Long DL, Preisser JS, Herring AH, Golin CE. A marginalized zero-inflated Poisson regression model with overall exposure effects. Stat Med. 2014;33(29):5151-5165. doi:10.1002/sim.6293
18. Kern LM, Safford MM, Slavin MJ, et al. Patients’ and providers’ views on causes and consequences of healthcare fragmentation in the ambulatory setting: a qualitative study. J Gen Intern Med. 2019;34(6):899-907. doi:10.1007/s11606-019-04859-1
19. Guo JY, Chou YJ, Pu C. Effect of continuity of care on drug-drug interactions. Med Care. 2017;55(8):744-751. doi:10.1097/mlr.0000000000000758
20. Fleming BB, Greenfield S, Engelgau MM, Pogach LM, Clauser SB, Parrott MA. The Diabetes Quality Improvement Project: moving science into health policy to gain an edge on the diabetes epidemic. Diabetes Care. 2001;24(10):1815. doi:10.2337/diacare.24.10.1815
21. O’Connor PJ, Bodkin NL, Fradkin J, et al. Diabetes performance measures: current status and future directions. Diabetes Care. 2011;34(7):1651-1659. doi:10.2337/dc11-0735
22. Calsbeek H, Ketelaar NABM, Faber MJ, Wensing M, Braspenning J. Performance measurements in diabetes care: the complex task of selecting quality indicators. Int J Qual Health Care. 2013;25(6):704-709. doi:10.1093/intqhc/mzt073
23. Huang YT, Steptoe A, Wei L, Zaninotto P. Polypharmacy difference between older people with and without diabetes: evidence from the English longitudinal study of ageing. Diabetes Res Clin Pract. 2021;176:108842. doi:10.1016/j.diabres.2021.108842
24. Schneider AL, Kalyani RR, Golden S, et al. Diabetes and prediabetes and risk of hospitalization: the Atherosclerosis Risk in Communities (ARIC) study. Diabetes Care. 2016;39(5):772-779. doi:10.2337/dc15-1335
25. Pilla SJ, Yeh HC, Juraschek SP, Clark JM, Maruthur NM. Predictors of insulin initiation in patients with type 2 diabetes: an analysis of the Look AHEAD randomized trial. J Gen Intern Med. 2018;33(6):839-846. doi:10.1007/s11606-017-4282-9
26. Barnett TE, Chumbler NR, Vogel WB, Beyth RJ, Qin H, Kobb R. The effectiveness of a care coordination home telehealth program for veterans with diabetes mellitus: a 2-year follow-up. Am J Manag Care. 2006;12(8):467-474.
27. Rawlins WS, Toscano-Garand MA, Graham G. Diabetes management with a care coordinator improves glucose control in African Americans and Hispanics. J Educ Health Promot. 2017;6:22. doi:10.4103/jehp.jehp_27_15
28. McLendon SF, Wood FG, Stanley N. Enhancing diabetes care through care coordination, telemedicine, and education: evaluation of a rural pilot program. Public Health Nurs. 2019;36(3):310-320. doi:10.1111/phn.12601
29. Bell SK, Gerard M, Fossa A, et al. A patient feedback reporting tool for OpenNotes: implications for patient-clinician safety and quality partnerships. BMJ Qual Saf. 2017;26(4):312-322. doi:10.1136/bmjqs-2016-006020
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