Inpatient diabetes: do-it-yourself electronic referral system to support and enhance the Think Glucose project

Detailed analysis of 'work as imagined' in the use of intravenous insulin infusions in a hospital: a hierarchical task analysis

OBJECTIVE

Variable rate intravenous insulin infusions (VRIII) is a high-risk medication that has a potential to cause significant patient harm if used in error. Complex preparation of VRIII in clinical areas and the need for frequent monitoring and adjustment increase the complexity of using VRIII. An emerging approach, called Resilient Healthcare, proposes understanding complexity of work by exploring how work is assumed to be done and compare it with everyday work. This study aimed to explore how VRIII is perceived to be used by healthcare practitioners, focusing on one aspect of Resilient Healthcare: understanding how work is assumed to be done, using a method called hierarchical task analysis (HTA).

DESIGN

A qualitative study using document analysis and focus groups.

SETTING

A vascular surgery unit in an acute National Health Service teaching hospital in the UK.

PARTICIPANTS

Stakeholders/users in different professional roles involved in the process of using VRIII.

RESULTS

The HTA showed the complexity of using VRIII and highlighted more than 115 steps required to treat elevated blood glucose. The process of producing hospital-specific guidelines was iterative. Careful consideration was taken to coordinate the development and implementation of guidelines. Documents provided detailed clinical instructions related to the use of VRIII but practitioners selectively used them, often in deference to senior colleagues. Intentional adaptations, for example, proactively asking for a VRIII prescription occurred and were acknowledged as part of providing individualised patient care.

CONCLUSION

Using VRIII to treat elevated blood glucose is a complex but necessary process mediated by a range of factors such as organisational influences. Adaptive strategies to mitigate errors were common and future research can build on insights from this study to develop a broader understanding of how VRIII is used and to understand how adaptations are made in relation to the use of VRIII.

Improving Referrals to Diabetes Self-Management Education in Medically Underserved Adults

OBJECTIVE

Electronic health records (EHRs) and clinical decision-support algorithms improve diabetes care. This quality improvement (QI) project aimed to determine whether an electronic diabetes education referral protocol using the Diabetes Self-Management Education and Support for Adults With Type 2 Diabetes: Algorithm of Care (DSMES Algorithm) and protocol training would increase the proportion of adult patients with type 2 diabetes at a federally qualified health center electronically referred for diabetes self-management education and support (DSMES).

DESIGN AND METHODS

The EHR was modified to include the DSMES Algorithm and questions regarding prior participation in diabetes education. Protocol trainings were conducted. Data were obtained via retrospective chart review. A one-sample t test was used to evaluate the statistical difference between the electronic referral (e-referral) rates of the pre-intervention and intervention groups.

RESULTS

Completion of the DSMES Algorithm was positively associated with e-referrals to diabetes education (P <0.001). The intervention group had a higher rate of e-referral for DSMES than the pre-intervention group (31 vs. 0%, P <0.001).

CONCLUSION

E-referral protocols using the DSMES Algorithm and protocol training may aid in the identification and documentation of self-care needs of medically underserved patients with type 2 diabetes and improve e-referrals to DSMES. Of clinical importance, these findings translate into active patient engagement, team-based care, and information-sharing. Additional work is needed to determine whether the e-referral rate is sustained or increases over time. Further investigations should also be explored to evaluate the impact of e-referral protocols and algorithms on participation in DSMES.

Clinical Prediction Tool To Identify Adults With Type 2 Diabetes at Risk for Persistent Adverse Glycemia in Hospital

OBJECTIVES

Given the high incidence of hyperglycemia and hypoglycemia in hospital and the lack of prediction tools for this problem, we developed a clinical tool to assist early identification of individuals at risk for persistent adverse glycemia (AG) in hospital.

METHODS

We analyzed a cohort of 594 consecutive adult inpatients with type 2 diabetes. We identified clinical factors available early in the admission course that were associated with persistent AG (defined as ≥2 days with capillary glucose <4 or >15 mmol/L during admission). A prediction model for persistent AG was constructed using logistic regression and internal validation was performed using a split-sample approach.

RESULTS

Persistent AG occurred in 153 (26%) of inpatients, and was associated with admission dysglycemia (odds ratio [OR], 3.65), glycated hemoglobin ≥8.1% (OR, 5.08), glucose-lowering treatment regimen containing sulfonylurea (OR, 3.50) or insulin (OR, 4.22), glucocorticoid medication treatment (OR, 2.27), Charlson Comorbidity Index score and the number of observed days. An early-identification prediction tool, based on clinical factors reliably available at admission (admission dysglycemia, glycated hemoglobin, glucose-lowering regimen and glucocorticoid treatment), could accurately predict persistent AG (receiver-operating characteristic area under curve = 0.806), and, at the optimal cutoff, the sensitivity, specificity and positive predictive value were 84%, 66% and 53%, respectively.

CONCLUSIONS

A clinical prediction tool based on clinical risk factors available at admission to hospital identified patients at increased risk for persistent AG and could assist early targeted management by inpatient diabetes teams.

Does the Diabetes Specialist Nursing workforce impact the experiences and outcomes of people with diabetes? A hermeneutic review of the evidence

OBJECTIVES

The aim of the hermeneutic review was to identify and clarify the mechanisms by which the Diabetes Specialist Nursing workforce affect the outcomes of diabetes patients, with a focus on those in the United Kingdom. A clarification of diabetes specialist nurses' work is necessary in understanding and improving diabetes inpatient care.

DESIGN

The design is a hermeneutic evidence review and was part of a wider evaluation of Diabetes Inpatient Specialist Nurses for which the evidence was sourced. The literature search was limited to specialist nursing workforce caring for adults with diabetes. In order to gain global understanding of the impact of specialist nursing in diabetes, worldwide literature was included.

METHODS

A hermeneutic literature review of 45 publications was carried out, which included citation analysis. Relevant literature was identified from 1990 to 2018.

RESULTS

Evidence suggests that Diabetes Specialist Nurses educate patients and other healthcare professionals as well as delivering direct care. The outcomes of these actions include a reduced patient length of stay in hospital, reduced inpatient harms and complications, and improved patient satisfaction. Additionally, they are cost-effective.

CONCLUSIONS

The Diabetes Specialist Nursing workforce is essential in diabetes care, particularly in hospital settings. They improve patient experience and outcomes.

Diabetes patient at risk score - a novel system for triaging appropriate referrals of inpatients with diabetes to the diabetes team

The acceptability, uptake and effectiveness of a new referral tool - the diabetes patient at risk (DPAR) score - were evaluated and the timeliness of review of referred inpatients by the diabetes team was measured. For this, a snapshot survey of ward healthcare professionals (HCPs) and a review of all DPAR referrals to the diabetes team between 1 September 2013 and 31 January 2014 were undertaken. All referrals in November 2013 were audited for timeliness of review. 77% of HCPs agreed/strongly agreed that the tool improved access to the diabetes team. 76% of referrals were from nurses. 80% of who should have been referred were referred; the remaining had already been reviewed by the diabetes team and therefore did not require referral. Only 11% of referrals were inappropriate. All DPAR referrals were reviewed within the stipulated time period in November 2013. Overall, the DPAR system was well accepted, successfully identified appropriate referrals and facilitated referrals in a timely manner to the diabetes team.