Inpatient to Outpatient Transfer of Care in Urban Patients With Diabetes

Wheeler, Kate; Crawford, Rochanda; McAdams, Debra; Benel, Sonia; Dunbar, Virginia G.; Caudle, Jane; George, Christopher D.; El‐Kebbi, Imad M.; Gallina, Daniel L.; Ziemer, David C.; Cook, Curtiss B.

doi:10.1001/archinte.164.4.447

Cited by 23 publications

(6 citation statements)

References 40 publications

(44 reference statements)

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“…Unfortunately, transition from hospital to home does not always go smoothly, resulting in an adverse event, poor glycemic control, and increased rate of emergency room visits ( 27 , 28 ) and higher hospital readmission rates and costs ( 28 ). One study estimated that 80% of serious medical errors involve miscommunication during the hand off between medical providers ( 29 ). To reduce both readmission rates and adverse events, hospitals must improve the effectiveness of transitions of care in which they play a role.…”

Section: Discussionmentioning

confidence: 99%

Hospital Discharge Algorithm Based on Admission HbA1c for the Management of Patients With Type 2 Diabetes

et al. 2014

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OBJECTIVEEffective treatment algorithms are needed to guide diabetes care at hospital discharge in general medicine and surgery patients with type 2 diabetes.RESEARCH DESIGN AND METHODSThis was a prospective, multicenter open-label study aimed to determine the safety and efficacy of a hospital discharge algorithm based on admission HbA1c. Patients with HbA1c <7% (53.0 mmol/mol) were discharged on their preadmission diabetes therapy, HbA1c between 7 and 9% (53.0–74.9 mmol/mol) were discharged on a preadmission regimen plus glargine at 50% of hospital daily dose, and HbA1c >9% were discharged on oral antidiabetes agents (OADs) plus glargine or basal bolus regimen at 80% of inpatient dose. The primary outcome was HbA1c concentration at 12 weeks after hospital discharge.RESULTSA total of 224 patients were discharged on OAD (36%), combination of OAD and glargine (27%), basal bolus (24%), glargine alone (9%), and diet (4%). The admission HbA1c was 8.7 ± 2.5% (71.6 mmol/mol) and decreased to 7.3 ± 1.5% (56 mmol/mol) at 12 weeks of follow-up (P < 0.001). The change of HbA1c from baseline at 12 weeks after discharge was −0.1 ± 0.6, −0.8 ± 1.0, and −3.2 ± 2.4 in patients with HbA1c <7%, 7–9%, and >9%, respectively (P < 0.001). Hypoglycemia (<70 mg/dL) was reported in 22% of patients discharged on OAD only, 30% on OAD plus glargine, 44% on basal bolus, and 25% on glargine alone and was similar in patients with admission HbA1c ≤7% (26%) compared with those with HbA1c >7% (31%, P = 0.54).CONCLUSIONSMeasurement of HbA1c on admission is beneficial in tailoring treatment regimens at discharge in general medicine and surgery patients with type 2 diabetes.

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Section: Discussionmentioning

confidence: 99%

Hospital Discharge Algorithm Based on Admission HbA1c for the Management of Patients With Type 2 Diabetes

et al. 2014

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“…Outside of HCV, this approach may be applied to evaluating service delivery for other conditions, especially those for which transitions of care between healthcare settings may be a barrier to quality or contribute to disparities. For instance, single‐institution studies have shown suboptimal rates of referral to HIV pre‐exposure prophylaxis for individuals diagnosed with sexually transmitted diseases in the emergency department, 19 or for hospitalized individuals with new diagnoses of diabetes 20 …”

Section: Discussionmentioning

confidence: 99%

“…For instance, single-institution studies have shown suboptimal rates of referral to HIV pre-exposure prophylaxis for individuals diagnosed with sexually transmitted diseases in the emergency department, 19 or for hospitalized individuals with new diagnoses of diabetes. 20 In addition, our approach can provide insights into the frequency of diagnosis across healthcare settings. This information can help healthcare administrators and policymakers streamline resource allocation, enhance facility planning, and ensure efficient patient management across healthcare settings.…”

Section: Determining the Setting Of Diagnosismentioning

confidence: 99%

Association between claims‐based setting of diagnosis and treatment initiation among Medicare patients with hepatitis C

Zhang,

Bao,

Hutchings

et al. 2024

Health Services Research

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ObjectiveTo develop a claims‐based algorithm to determine the setting of a disease diagnosis.Data Sources and Study SettingMedicare enrollment and claims data from 2014 to 2019.Study DesignWe developed a claims‐based algorithm using facility indicators, revenue center codes, and place of service codes to identify settings where HCV diagnosis first appeared. When the first appearance was in a laboratory, we attempted to associate HCV diagnoses with subsequent clinical visits. Face validity was assessed by examining association of claims‐based diagnostic settings with treatment initiation.Data Collection/Extraction MethodsPatients newly diagnosed with HCV and continuously enrolled in traditional Medicare Parts A, B, and D (12 months before and 6 months after index diagnosis) were included.Principal FindingsAmong 104,454 patients aged 18–64 and 66,726 aged ≥65, 70.1% and 69%, respectively, were diagnosed in outpatient settings, and 20.2% and 22.7%, respectively in laboratory or unknown settings. Logistic regression revealed significantly lower odds of treatment initiation after diagnosis in emergency departments/urgent cares, hospitals, laboratories, or unclassified settings, than in outpatient visits.ConclusionsThe algorithm identified the setting of HCV diagnosis in most cases, and found significant associations with treatment initiation, suggesting an approach that can be adapted for future claims‐based studies.

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“…Insufficient monitoring equipment and treatments for diabetes have been described in sub-Saharan Africa [34] and understanding the overall volume of transfers involving PHC facilities is vital to improving the availability of diabetes care at PHC level. Research on outcomes of transfers among PLD has focused on up-referrals [35][36][37] and downreferrals [38][39][40][41][42][43][44][45]. In particular, PLD who are discharged from the hospital are at risk of loss to follow-up and readmission, and numerous interventions have been tested to improve outcomes [38][39][40][41][43][44][45].…”

Section: Introductionmentioning

confidence: 99%

“…Research on outcomes of transfers among PLD has focused on up-referrals [35][36][37] and downreferrals [38][39][40][41][42][43][44][45]. In particular, PLD who are discharged from the hospital are at risk of loss to follow-up and readmission, and numerous interventions have been tested to improve outcomes [38][39][40][41][43][44][45]. However, data on outcomes of transfers between PHC facilities among PLD are limited.…”

Section: Introductionmentioning

confidence: 99%

Transfers between health facilities of people living with diabetes attending primary health care services in the Western Cape Province of South Africa: A retrospective cohort study

Odayar,

Rusch,

Dave

et al. 2024

Tropical Med Int Health

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ObjectivesTransfers between health facilities of people living with HIV attending primary health care (PHC) including hospital to PHC facility, PHC facility to hospital and PHC facility to PHC facility transfers occur frequently, affect health service planning, and are associated with disengagement from care and viraemia. Data on transfers among people living with diabetes attending PHC, particularly transfers between PHC facilities, are few. We assessed the transfer incidence rate of people living with diabetes attending PHC, and the association between transfers between PHC facilities and subsequent HbA1c values.MethodsWe analysed data on HbA1c tests at public sector facilities in the Western Cape Province (2016–March 2020). Individuals with an HbA1c in 2016–2017 were followed‐up for 27 months and included in the analysis if ≥18 years at first included HbA1c, ≥2 HbA1cs during follow‐up and ≥1 HbA1c at a PHC facility. A visit interval was the duration between two consecutive HbA1cs. Successive HbA1cs at different facilities of any type indicated any transfer, and HbA1cs at different PHC facilities indicated a transfer between PHC facilities. Mixed effects logistic regression adjusted for sex, age, rural/urban facility attended at the start of the visit interval, disengagement (visit interval >14 months) and a hospital visit during follow‐up assessed the association between transfers between PHC facilities and HbA1c >8%.ResultsAmong 102,813 participants, 22.6% had ≥1 transfer of any type. Including repeat transfers, there were 29,994 transfers (14.4 transfers per 100 person‐years, 95% confidence interval [CI] 14.3–14.6). A total of 6996 (30.1%) of those who transferred had a transfer between PHC facilities. Visit intervals with a transfer between PHC facilities were longer (349 days, interquartile range [IQR] 211–503) than those without any transfer (330 days, IQR 182–422). The adjusted relative odds of an HbA1c ≥8% after a transfer between PHC facilities versus no transfer were 1.20 (95% CI 1.05–1.37).ConclusionThe volume of transfers involving PHC facilities requires consideration when planning services. Individuals who transfer between PHC facilities require additional monitoring and support.

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Inpatient to Outpatient Transfer of Care in Urban Patients With Diabetes

Cited by 23 publications

References 40 publications

Hospital Discharge Algorithm Based on Admission HbA1c for the Management of Patients With Type 2 Diabetes

Hospital Discharge Algorithm Based on Admission HbA1c for the Management of Patients With Type 2 Diabetes

Association between claims‐based setting of diagnosis and treatment initiation among Medicare patients with hepatitis C

Transfers between health facilities of people living with diabetes attending primary health care services in the Western Cape Province of South Africa: A retrospective cohort study

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