Clinical decision support systems in the care of inpatients with diabetes in non-critical care setting: systematic review

The effect of Orem self-care model on the improvement of symptoms and quality of life in patients with diabetes: A scoping review

Objective

to evaluate the association of Orem self-care model improvement of symptoms and quality of life in patients with diabetes.

Methods

A scoping review was carried on bibliographic databases: PubMed-Medline, Scopus, SID and Magiran. The inclusion criteria encompassed studies examining the impact of the Orem self-care model on diabetic patients. Studies considered for inclusion needed to have full-text availability and be written in either English or Persian, with key words including "Models", "Nursing", "Quality of Life", and "Diabetes Mellitus". CONSORT checklist and STROBE statement were selected for quality assessment.

Results

A total of 9 studies were included, all using quantitative methodology and focusing on adults or older adults. The majority of articles focused on quality of life and diabetic symptoms. 8 studies showed positive outcomes after implementation of the model. The findings indicate that this model led to an enhanced level of self-efficacy, improved quality of life, and better self-care practices among diabetic patients.

Conclusion

Orem self-care model can reduce the diabetic symptoms and improve the quality of life, self-efficacy and self-care in these patients.

Applications of Clinical Decision Support Systems in Diabetes Care: Scoping Review

BACKGROUND

Providing comprehensive and individualized diabetes care remains a significant challenge in the face of the increasing complexity of diabetes management and a lack of specialized endocrinologists to support diabetes care. Clinical decision support systems (CDSSs) are progressively being used to improve diabetes care, while many health care providers lack awareness and knowledge about CDSSs in diabetes care. A comprehensive analysis of the applications of CDSSs in diabetes care is still lacking.

OBJECTIVE

This review aimed to summarize the research landscape, clinical applications, and impact on both patients and physicians of CDSSs in diabetes care.

METHODS

We conducted a scoping review following the Arksey and O'Malley framework. A search was conducted in 7 electronic databases to identify the clinical applications of CDSSs in diabetes care up to June 30, 2022. Additional searches were conducted for conference abstracts from the period of 2021-2022. Two researchers independently performed the screening and data charting processes.

RESULTS

Of 11,569 retrieved studies, 85 (0.7%) were included for analysis. Research interest is growing in this field, with 45 (53%) of the 85 studies published in the past 5 years. Among the 58 (68%) out of 85 studies disclosing the underlying decision-making mechanism, most CDSSs (44/58, 76%) were knowledge based, while the number of non-knowledge-based systems has been increasing in recent years. Among the 81 (95%) out of 85 studies disclosing application scenarios, the majority of CDSSs were used for treatment recommendation (63/81, 78%). Among the 39 (46%) out of 85 studies disclosing physician user types, primary care physicians (20/39, 51%) were the most common, followed by endocrinologists (15/39, 39%) and nonendocrinology specialists (8/39, 21%). CDSSs significantly improved patients' blood glucose, blood pressure, and lipid profiles in 71% (45/63), 67% (12/18), and 38% (8/21) of the studies, respectively, with no increase in the risk of hypoglycemia.

CONCLUSIONS

CDSSs are both effective and safe in improving diabetes care, implying that they could be a potentially reliable assistant in diabetes care, especially for physicians with limited experience and patients with limited access to medical resources.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.37766/inplasy2022.9.0061.

Using Technology to Improve Diabetes Care in Hospital: The Challenge and the Opportunity

The past 10 years have seen a revolution in technology improving the lives of people with diabetes. This has implications for diabetes care in hospitalized inpatients. These technological developments have the potential to significantly improve the care of people with diabetes in hospital. Combining point of care glucose monitoring, electronic prescribing, electronic observations with electronic referral, and electronic health records allow teams to daily oversee the whole hospital population. To make the most of these tools as well as developing the use of pumps and glucose sensors in hospital, the diabetes team needs to work in new ways. To date, very little work has described how these should be combined. We describe how this technology can be combined to improve diabetes care in hospital.

Clinical Decision Support for Glycemic Management Reduces Hospital Length of Stay

OBJECTIVE

Dysglycemia influences hospital outcomes and resource utilization. Clinical decision support (CDS) holds promise for optimizing care by overcoming management barriers. This study assessed the impact on hospital length of stay (LOS) of an alert-based CDS tool in the electronic medical record that detected dysglycemia or inappropriate insulin use, coined as gaps in care (GIC).

RESEARCH DESIGN AND METHODS

Using a 12-month interrupted time series among hospitalized persons aged ≥18 years, our CDS tool identified GIC and, when active, provided recommendations. We compared LOS during 6-month-long active and inactive periods using linear models for repeated measures, multiple comparison adjustment, and mediation analysis.

RESULTS

Among 4,788 admissions with GIC, average LOS was shorter during the tool's active periods. LOS reductions occurred for all admissions with GIC (-5.7 h, P = 0.057), diabetes and hyperglycemia (-6.4 h, P = 0.054), stress hyperglycemia (-31.0 h, P = 0.054), patients admitted to medical services (-8.4 h, P = 0.039), and recurrent hypoglycemia (-29.1 h, P = 0.074). Subgroup analysis showed significantly shorter LOS in recurrent hypoglycemia with three events (-82.3 h, P = 0.006) and nonsignificant in two (-5.2 h, P = 0.655) and four or more (-14.8 h, P = 0.746). Among 22,395 admissions with GIC (4,788, 21%) and without GIC (17,607, 79%), LOS reduction during the active period was 1.8 h (P = 0.053). When recommendations were provided, the active tool indirectly and significantly contributed to shortening LOS through its influence on GIC events during admissions with at least one GIC (P = 0.027), diabetes and hyperglycemia (P = 0.028), and medical services (P = 0.019).

CONCLUSIONS

Use of the alert-based CDS tool to address inpatient management of dysglycemia contributed to reducing LOS, which may reduce costs and improve patient well-being.

Clinical Decision Support for Diabetes Care in the Hospital: A Time for Change Toward Improvement of Management and Outcomes

The increasing prevalence of diabetes permeates hospitals and dysglycemia is associated with poor clinical and economic outcomes. Despite endorsed guidelines, barriers to optimal management and gaps in care prevail. Providers' limitations on knowledge, attitudes, and decision-making about hospital diabetes management are common. This adds to the complexity of dispersed glucose and insulin dosing data within medical records. This creates a dichotomy as safe and effective care are key objectives of healthcare organizations. This perspective highlights evidence of the benefits of clinical decision support (CDS) in hospital glycemic management. It elaborates on barriers CDS can help resolve, and factors driving its success. CDS represents a resource to individualize care and improve outcomes. It can help overcome a multifactorial problem impacting patients' lives on a daily basis.

Clinical decision support systems with team-based care on type 2 diabetes improvement for Medicaid patients: A quality improvement project

BACKGROUND

The prevalence of clinical inertia, the failure of appropriate treatment intensification in diabetes treatment, is a well-documented worldwide phenomenon. This project addresses the problem of clinical inertia through three interrelated activities, clinical decision support (CDSS), team-based care, and patient engagement in diabetes management.

OBJECTIVES

The purpose of this research is to provide analysis under the State-University Partnership Learning Network regarding the impact of an electronic decision support tool combined with team-based care workflow on provider decision-making and patient outcomes for the treatment of poorly controlled diabetes mellitus (diabetes) among patients receiving Kentucky Medicaid. The objectives of this study are to 1) assess clinical outcomes of type 2 diabetes in the Medicaid population with team-based care using CDSS, 2) evaluate physicians' and pharmacists' experience on CDSS.

METHODS

This is a quality improvement project using a mixed-method - longitudinal and control group comparison of outcomes based upon clinical measures and online surveys of providers and pharmacists involved in this project.

RESULTS

Patients treated by providers who changed the treatment regimen to one that either fully or partially followed the recommendation of the CDSS tool had a statistically significant reduction in HbA1c with an average initial HbA1c of 10.1 and the final HbA1c of 8. The online survey of physicians shows that more than 80% of physicians agree the use of CDSS will support improved patient outcomes. The use of a team-based care approach that includes pharmacists in implementing treatment changes was broadly supported by both physicians and pharmacists.

CONCLUSION

CDSS combined with team-based care can be effective in reducing HbA1c to targeted therapeutic levels. The use of CDSS provides a way to efficiently assess more than 160 potential frontline drugs and properly accelerate treatment. Consistent with the research literature, the inclusion of pharmacists can play a key role in team-based care to assess treatment alternatives and provide for improvement in outcomes and patient adherence for diabetes. The user surveys show both physicians and pharmacists have a positive attitude toward CDSS.

Digital interventions to improve safety and quality of inpatient diabetes management: A systematic review

IMPORTANCE

Diabetes is common amongst hospitalised patients and contributes to increased length of stay and poorer outcomes. Digital transformation, particularly the implementation of electronic medical records (EMRs), is rapidly occurring across the healthcare sector and provides an opportunity to improve the safety and quality of inpatient diabetes care. Alongside this revolution has been a considerable and ongoing evolution of digital interventions to optimise care of inpatients with diabetes including optimisation of EMRs, digital clinical decision support systems (CDSS) and solutions utilising data visibility to allow targeted patient review.

OBJECTIVE

To systematically appraise the recent literature to determine which digitally-enabled interventions including EMR, CDSS and data visibility solutions improve the safety and quality of non-critical care inpatient diabetes management.

METHODS

Pubmed, Embase and Cochrane databases were searched for suitable articles. Selected articles underwent quality assessment and analysis with results grouped by intervention type.

RESULTS

1202 articles were identified with 42 meeting inclusion criteria. Four key interventions were identified; computerised physician order entry (n = 4), clinician decision support systems (n = 21), EMR driven active case finding (data visibility solutions) and targeted patient review (n = 10) and multicomponent system interventions (n = 7). Studies reported on glucometric outcomes, evidence-based medication ordering including medication errors, and patient and user outcomes. An improvement in glucometric measures particularly mean blood glucose and proportion of target range blood glucose levels and rates of evidence-based insulin prescribing were consistently demonstrated.

CONCLUSION

Digitally-enabled interventions utilised to improve quality and safety of inpatient diabetes care were heterogenous in design. The majority of studies across all intervention types reported positive effects for evidence-based prescribing and glucometric outcomes. There was less evidence for digital interventions reducing diabetes medication administration errors or impacting patient outcomes (length of stay).

Clinical decision support to improve management of diabetes and dysglycemia in the hospital: a path to optimizing practice and outcomes

INTRODUCTION

Innovative approaches are needed to design robust clinical decision support (CDS) to optimize hospital glycemic management. We piloted an electronic medical record (EMR), evidence-based algorithmic CDS tool in an academic center to alert clinicians in real time about gaps in care related to inpatient glucose control and insulin utilization, and to provide management recommendations.

RESEARCH DESIGN AND METHODS

The tool was designed to identify clinical situations in need for action: (1) severe or recurrent hyperglycemia in patients with diabetes: blood glucose (BG) ≥13.88 mmol/L (250 mg/dL) at least once or BG ≥10.0 mmol/L (180 mg/dL) at least twice, respectively; (2) recurrent hyperglycemia in patients with stress hyperglycemia: BG ≥10.0 mmol/L (180 mg/dL) at least twice; (3) impending or established hypoglycemia: BG 3.9-4.4 mmol/L (70-80 mg/dL) or ≤3.9 mmol/L (70 mg/dL); and (4) inappropriate sliding scale insulin (SSI) monotherapy in recurrent hyperglycemia, or anytime in patients with type 1 diabetes. The EMR CDS was active (ON) for 6 months for all adult hospital patients and inactive (OFF) for 6 months. We prospectively identified and compared gaps in care between ON and OFF periods.

RESULTS

When active, the hospital CDS tool significantly reduced events of recurrent hyperglycemia in patients with type 1 and type 2 diabetes (3342 vs 3701, OR=0.88, p=0.050) and in patients with stress hyperglycemia (288 vs 506, OR=0.60, p<0.001). Hypoglycemia or impending hypoglycemia (1548 vs 1349, OR=1.15, p=0.050) were unrelated to the CDS tool on subsequent analysis. Inappropriate use of SSI monotherapy in type 1 diabetes (10 vs 22, OR=0.36, p=0.073), inappropriate use of SSI monotherapy in type 2 diabetes (2519 vs 2748, OR=0.97, p=0.632), and in stress hyperglycemia subjects (1617 vs 1488, OR=1.30, p<0.001) were recognized.

CONCLUSION

EMR CDS was successful in reducing hyperglycemic events among hospitalized patients with dysglycemia and diabetes, and inappropriate insulin use in patients with type 1 diabetes.

Medical software applications for in-hospital insulin therapy: A systematic review

Background

In-hospital hyperglycemia (HH) is frequent and related to higher morbidity and mortality. Despite the benefits of HH treatment, glycemic control is often poor and neglected. The use of health applications to support diagnosis and therapy is now incorporated into medical practice. Medical applications for inpatient glycemic management have potential to standardize this handling by the nonspecialist physician. However, related studies are scarce. We aim to evaluate the efficacy in inpatient glycemic control parameters of medical software applications in non-critical care settings.

Methods

This systematic review on in-hospital insulin applications was performed according to PRISMA guidelines. Data were extracted in triplicate and methodological quality was verified. Specific outcomes of interest were glycemic control efficacy, hypoglycemia risk, length of in-hospital stay, integration with the electronic medical record and healthcare staff acceptance.

Results

Among the 573 articles initially identified and subsequent revision of the references of each one, seven studies involving six applications were eligible for the review. A better glycemic control was reported with the use of most in-hospital insulin applications in the studies evaluated, but there was no mention of the time to reach the glycemic goal. The risk of hypoglycemia was low. Different reasons influenced the varied acceptance of the use of applications among health professionals.

Conclusion

The six applications of inpatient insulin therapy in a non-critical care environment proved to be useful and safe compared to the usual management. Medical apps are tools that can help improve the quality of patient care.

Guiding Glucose Management Discussions Among Adults With Type 2 Diabetes in General Practice: Development and Pretesting of a Clinical Decision Support Tool Prototype Embedded in an Electronic Medical Record

BACKGROUND

Managing type 2 diabetes (T2D) requires progressive lifestyle changes and, sometimes, pharmacological treatment intensification. General practitioners (GPs) are integral to this process but can find pharmacological treatment intensification challenging because of the complexity of continually emerging treatment options.

OBJECTIVE

This study aimed to use a co-design method to develop and pretest a clinical decision support (CDS) tool prototype (GlycASSIST) embedded within an electronic medical record, which uses evidence-based guidelines to provide GPs and people with T2D with recommendations for setting glycated hemoglobin (HbA1c) targets and intensifying treatment together in real time in consultations.

METHODS

The literature on T2D-related CDS tools informed the initial GlycASSIST design. A two-part co-design method was then used. Initial feedback was sought via interviews and focus groups with clinicians (4 GPs, 5 endocrinologists, and 3 diabetes educators) and 6 people with T2D. Following refinements, 8 GPs participated in mock consultations in which they had access to GlycASSIST. Six people with T2D viewed a similar mock consultation. Participants provided feedback on the functionality of GlycASSIST and its role in supporting shared decision making (SDM) and treatment intensification.

RESULTS

Clinicians and people with T2D believed that GlycASSIST could support SDM (although this was not always observed in the mock consultations) and individualized treatment intensification. They recommended that GlycASSIST includes less information while maintaining relevance and credibility and using graphs and colors to enhance visual appeal. Maintaining clinical autonomy was important to GPs, as they wanted the capacity to override GlycASSIST's recommendations when appropriate. Clinicians requested easier screen navigation and greater prescribing guidance and capabilities.

CONCLUSIONS

GlycASSIST was perceived to achieve its purpose of facilitating treatment intensification and was acceptable to people with T2D and GPs. The GlycASSIST prototype is being refined based on these findings to prepare for quantitative evaluation.

In guidelines physicians trust? Physician perspective on adherence to medical guidelines for type 2 diabetes mellitus

AIMS

Adherence to treatment guidelines and treatment success are low in Type 2 diabetes mellitus (T2DM). This study aims to capture the physician perspective on T2DM guideline adherence and identify levers for increasing adherence.

METHODS

A survey among German physicians captured the perceived value of 4 areas in the national treatment guideline (NVL), 13 possible barriers, and 9 possible enablers for guideline adherence. Perceived value was assessed by ranking 4 NVL areas by implementation difficulty and impact on treatment success. Barriers and enablers were assessed by rating their influence on guideline deviation and adherence. The consistency of results across subgroups was assessed using Fisher's exact test.

RESULTS

Responses from 46 physicians showed a strong consensus about the value of each NVL area. Physicians perceived patient inability and demotivation to be the strongest adherence barriers (93%, 78%). All queried enablers were approved by ≥ 50% of participants. Physicians considered cross-provider collaboration and electronic therapy decision support as strongest enablers (85%, 80%). Consistency was high between subgroups.

CONCLUSION

This study suggests that physicians consider patient-related factors to be stronger barriers for guideline adherence than physician-related factors. Finding opportunities to increase physician buy-in is important for better guideline adherence. In this study, physicians voiced appreciation for adherence enablers based on digital solutions to support the care process and to reduce the complexity of therapy decisions.

The effects of clinical decision support systems on insulin use: A systematic review

BACKGROUND

A clinical decision support system (CDSS) is a computerized system using case-based reasoning to assist clinicians in assessing disease status, in selecting appropriate therapy or in making other clinical decisions. Previous randomized controlled trials (RCTs or trials) have shown that CDSSs have the potential to improve the insulin use, but the evidence was conflicting and uncertain. The purpose of our study was to determine whether a CDSS improves the use of insulin.

METHOD

PubMed, Embase, Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov were searched from their inception to October 2018. The quality assessment was based on the risk of bias criteria of the Cochrane Handbook.

RESULTS

Twenty-four RCTs, involving 7653 participants, were included. Thirteen of those trials (54.2%) used a computerized algorithm or a computer-assisted insulin protocol for insulin dose and therapy adjustment, of which 30.8% (four of 13) found significant changes. Of 10 trials that measured mean blood glucose levels and the 11 trials reported HbA1c, the computerized insulin dose adjustment resulted in lower mean blood glucose levels in 70.0% (seven of 10) and 36.4% (four of 11) of RCTs, respectively. Additionally, a significant reduction of hyperglycaemia events was reported in three of six RCTs. The evidence in a majority of the 24 RCTs was of moderate quality.

CONCLUSIONS

CDSSs have the potential to improve the insulin use and blood glucose control in a clinical setting. The methodologies in these studies were of mixed quality. Better designed and longer-term studies are required to ensure a larger and more reliable evidence base on the effects of CDSS intervention on insulin use.

Timing of Insulin with Meals in the Hospital: a Systems Improvement Approach

PURPOSE OF REVIEW

Coordination of glucose monitoring, mealtimes, and insulin delivery in the hospital is complex, involving interactions between multiple key agents and overlapping workflows. The purpose of this review is to evaluate the scope of the problem as well as to assess evidence for interventions.

RECENT FINDINGS

In recent years, there has been an emphasis on systems-based approaches which address multiple contributing components of the problem at once in an effort to more seamlessly integrate workflows. Technological advances, such as decision support systems and advances in automated insulin delivery, and strategies that minimize the need for complex insulin regimens hold promise for future study. Evaluation of the coordination of insulin delivery is limited by a lack of standardized metrics and systematically collected mealtimes. Nevertheless, successful efforts include system-wide multicomponent interventions, though advances in therapeutic approaches may be of value.

Use of a real-time, algorithm-driven, publicly displayed, automated signal to improve insulin prescribing practices

AIM

The clinical andon board (CAB) is a novel electronic surveillance and communication system, which alerts providers to and prompts treatment of dysglycemia. This investigation was designed to determine the CAB's effectiveness in supporting adherence to standardized evidence-based protocols, as well as improving glycemic control.

METHODS

This study was a retrospective pre/post analysis of insulin orders and blood glucose values. We used a Student's t-test for continuous variables and Chi² for all other variables. This study included patients 18 years or older admitted to the hospital medical service as an inpatient with a length of stay greater than 24 h and less than 90 days. We used Pearson's correlation coefficient to evaluate the relationship between CAB and blood glucose.

RESULTS

The rate of compliance in prescribing basal insulin for patient with diabetes increased from 56% to 77% (p < 0.001). Similarly, compliance rates for prescribing correctional insulin in patients without diabetes increased from 15% to 37% (p < 0.001). Performance on the CAB was linearly related to blood glucose (p = 0.004), and there was a small statistically (not clinically) significant improvement in mean blood glucose values.

CONCLUSION

This approach is effective in alerting and engaging providers to prescribe insulin in a standardized manner with potential to improve glycemic control.

Importance of inpatient hypoglycaemia: impact, prediction and prevention

Hypoglycaemia is a key barrier to achieving euglycaemic control in people who are hospitalized. Inpatient hypoglycaemia has been linked to adverse clinical outcomes, including mortality and longer stay in hospital. A number of studies have applied mathematical tools and statistical models to predict inpatient hypoglycaemia and identify factors that may result in hypoglycaemic events. Several different approaches have been tested to prevent inpatient hypoglycaemia. These can be categorized as human intervention, computerized methods or application of medical devices. In this review we provide an overview of the epidemiology of inpatient hypoglycaemia and its impact on patients and hospitals. We also discuss the existing methodology used to predict inpatient hypoglycaemia and the limited number of trials performed to prevent inpatient hypoglycaemia. The review highlights the urgent need for evidence-based methods to reduce inpatient hypoglycaemia.

Evaluation of clinical decision support systems for diabetes care: An overview of current evidence

BACKGROUND

Systematic reviews (SRs) have shown that clinical decision support systems (CDSSs) have the potential to improve diabetes care. However, methods of measuring and presenting outcomes are varied, and conclusions have been inconsistent. In addition, the reporting and methodological quality in this field is unknown, which could affect the integrity and accuracy of research. Therefore, it is difficult to confirm whether CDSSs are effective in improving diabetes care.

OBJECTIVE

To comprehensively evaluate the effects of CDSS on diabetes care and to examine the methodological and reporting qualities.

METHODS

We searched PubMed, EMBASE, and Cochrane Library from their inception to February 2017. Systematic reviews investigating the effects of CDSS on diabetes care were included. Outcomes were determined in advance and assessed separately for process of care and patient outcomes. Methodological and reporting qualities were assessed by AMSTAR and PRISMA, respectively.

RESULTS

Seventeen SRs, consisting of 222 unique randomized controlled trials and 102 nonrandomized controlled trials, were included. Evidence that CDDS significantly impacted patient outcomes was found in 32 of 102 unique studies of the 15 SRs that examined this effect (31%). A significant impact of CDSS on process of care was found in 117 out of 143 unique studies of the 11 SRs that examined this effect (82%). Ratings for overall scores of AMSTAR resulted in a mean score of 6.5 with a range of scores from 3.5 to 10.0. Reporting quality related to methodological domains was particularly incomplete.

CONCLUSIONS

Clinical decision support systems improved the quality of diabetes care by inconsistently improving process of care or patient outcomes. There is evidence that CDSS for providing alerts, reminders, or feedback to participants were most likely to impact diabetes care. Poor reporting of methodological domains, together with qualitative or narrative methods to combine findings, may limit the confidence in research evidence.

Eye of the beholder: Risk calculators and barriers to adoption in surgical trainees

BACKGROUND

Accurate risk assessment before surgery is complex and hampered by behavioral factors. Underutilized risk-based decision-support tools may counteract these barriers. The purpose of this study was to identify perceptions of and barriers to the use of surgical risk-assessment tools and assess the importance of data framing as a barrier to adoption in surgical trainees.

METHODS

We distributed a survey and risk assessment activity to surgical trainees at four training institutions. The primary outcomes of this study were descriptive risk assessment practices currently performed by residents, identifiable influences and obstacles to adoption, and the variability of preference sets when comparing modified System Usability Scores of a current risk calculator to a purpose-built calculator revision. Risk calculator comparison responses were compared with simple and multivariable regression to identify predictors for preferentiality.

RESULTS

We collected responses from 124 surgical residents (39% response rate). Participants endorsed familiarity with direct verbal communication (100%), sketch diagrams (87%), and brochures (59%). The most contemporary risk communication frameworks, such as best-worst case scenario framing (38%), case-specific risk calculators (43%), and all-procedure calculators (52%) were the least familiar. Usage favored traditional models of communication with only 26% of residents regularly using a strategy other than direct verbal discussion or anatomic sketch diagrams. Barriers limiting routine use included lack of electronic and clinical workflow integration. The mean modified System Usability Scores domain scores were widely dispersed for all domains, and no domain demonstrated one calculator's superiority over another.

CONCLUSION

Risk assessment tools are underutilized by trainees. Of importance, preference sets of clinicians appear to be unpredictable and may benefit more from a customizable, bespoke approach.

Diabetes and the direct secondary use of electronic health records: Using routinely collected and stored data to drive research and understanding

Introduction

Electronic health records provide an unparalleled opportunity for the use of patient data that is routinely collected and stored, in order to drive research and develop an epidemiological understanding of disease. Diabetes, in particular, stands to benefit, being a data-rich, chronic-disease state. This article aims to provide an understanding of the extent to which the healthcare sector is using routinely collected and stored data to inform research and epidemiological understanding of diabetes mellitus.

Methods

Narrative literature review of articles, published in both the medical- and engineering-based informatics literature.

Results

There has been a significant increase in the number of papers published, which utilise electronic health records as a direct data source for diabetes research. These articles consider a diverse range of research questions. Internationally, the secondary use of electronic health records, as a research tool, is most prominent in the USA. The barriers most commonly described in research studies include missing values and misclassification, alongside challenges of establishing the generalisability of results.

Discussion

Electronic health record research is an important and expanding area of healthcare research. Much of the research output remains in the form of conference abstracts and proceedings, rather than journal articles. There is enormous opportunity within the United Kingdom to develop these research methodologies, due to national patient identifiers. Such a healthcare context may enable UK researchers to overcome many of the barriers encountered elsewhere and thus to truly unlock the potential of electronic health records.

Bridging technology and clinical practice: innovating inpatient hyperglycaemia management in non-critical care settings

Emerging evidence shows that suboptimal glycaemic control is associated with increased morbidity and length of stay in hospital. Various guidelines for safe and effective inpatient glycaemic control in the non-critical care setting have been published. In spite of this, implementation in practice remains limited because of the increasing number of people with diabetes admitted to hospital and staff work burden. The use of technology in the outpatient setting has led to improved glycaemic outcomes and quality of life for people with diabetes. There remains an unmet need for technology utilisation in inpatient hyperglycaemia management in the non-critical care setting. Novel technologies have the potential to provide benefits in diabetes care in hospital by improving efficacy, safety and efficiency. Rapid analysis of glucose measurements by point-of-care devices help facilitate clinical decision-making and therapy adjustment in the hospital setting. Glucose treatment data integration with computerized glucose management systems underpins the effective use of decision support systems and may streamline clinical staff workflow. Continuous glucose monitoring and automation of insulin delivery through closed-loop systems may provide a safe and efficacious tool for hospital staff to manage inpatient hyperglycaemia whilst reducing staff workload. This review summarizes the evidence with regard to technological methods to manage inpatient glycaemic control, their limitations and the future outlook, as well as potential strategies by healthcare organizations such as the National Health Service to mediate the adoption, procurement and use of diabetes technologies in the hospital setting.

The Case for Diabetes Population Health Improvement: Evidence-Based Programming for Population Outcomes in Diabetes

PURPOSE OF REVIEW

The goal of this review is to describe diabetes within a population health improvement framework and to review the evidence for a diabetes population health continuum of intervention approaches, including diabetes prevention and chronic and acute diabetes management, to improve clinical and economic outcomes.

RECENT FINDINGS

Recent studies have shown that compared to usual care, lifestyle interventions in prediabetes lower diabetes risk at the population-level and that group-based programs have low incremental medial cost effectiveness ratio for health systems. Effective outpatient interventions that improve diabetes control and process outcomes are multi-level, targeting the patient, provider, and healthcare system simultaneously and integrate community health workers as a liaison between the patient and community-based healthcare resources. A multi-faceted approach to diabetes management is also effective in the inpatient setting. Interventions shown to promote safe and effective glycemic control and use of evidence-based glucose management practices include provider reminder and clinical decision support systems, automated computer order entry, provider education, and organizational change. Future studies should examine the cost-effectiveness of multi-faceted outpatient and inpatient diabetes management programs to determine the best financial models for incorporating them into diabetes population health strategies.

Development of a clinical decision support system for diabetes care: A pilot study

Management of complex chronic diseases such as diabetes requires the assimilation and interpretation of multiple laboratory test results. Traditional electronic health records tend to display laboratory results in a piecemeal and segregated fashion. This makes the assembly and interpretation of results related to diabetes care challenging. We developed a diabetes-specific clinical decision support system (Diabetes Dashboard) interface for displaying glycemic, lipid and renal function results, in an integrated form with decision support capabilities, based on local clinical practice guidelines. The clinical decision support system included a dashboard feature that graphically summarized all relevant laboratory results and displayed them in a color-coded system that allowed quick interpretation of the metabolic control of the patients. An alert module informs the user of tests that are due for repeat testing. An interactive graph module was also developed for better visual appreciation of the trends of the laboratory results of the patient. In a pilot study involving case scenarios administered via an electronic questionnaire, the Diabetes Dashboard, compared to the existing laboratory reporting interface, significantly improved the identification of abnormal laboratory results, of the long-term trend of the laboratory tests and of tests due for repeat testing. However, the Diabetes Dashboard did not significantly improve the identification of patients requiring treatment adjustment or the amount of time spent on each case scenario. In conclusion, we have developed and shown that the use of the Diabetes Dashboard, which incorporates several decision support features, can improve the management of diabetes. It is anticipated that this dashboard will be most helpful when deployed in an outpatient setting, where physicians can quickly make clinical decisions based on summarized information and be alerted to pertinent areas of care that require additional attention.

Going digital: a narrative overview of the clinical and organisational impacts of eHealth technologies in hospital practice

Objective The aim of the present study was to determine the effects of hospital-based eHealth technologies on quality, safety and efficiency of care and clinical outcomes. Methods Systematic reviews and reviews of systematic reviews of eHealth technologies published in PubMed/Medline/Cochrane Library between January 2010 and October 2015 were evaluated. Reviews of implementation issues, non-hospital settings or remote care or patient-focused technologies were excluded from analysis. Methodological quality was assessed using a validated appraisal tool. Outcome measures were benefits and harms relating to electronic medical records (EMRs), computerised physician order entry (CPOE), electronic prescribing (ePrescribing) and computerised decision support systems (CDSS). Results are presented as a narrative overview given marked study heterogeneity. Results Nineteen systematic reviews and two reviews of systematic reviews were included from 1197 abstracts, nine rated as high quality. For EMR functions, there was moderate-quality evidence of reduced hospitalisations and length of stay and low-quality evidence of improved organisational efficiency, greater accuracy of information and reduced documentation and process turnaround times. For CPOE functions, there was moderate-quality evidence of reductions in turnaround times and resource utilisation. For ePrescribing, there was moderate-quality evidence of substantially fewer medications errors and adverse drug events, greater guideline adherence, improved disease control and decreased dispensing turnaround times. For CDSS, there was moderate-quality evidence of increased use of preventive care and drug interaction reminders and alerts, increased use of diagnostic aids, more appropriate test ordering with fewer tests per patient, greater guideline adherence, improved processes of care and less disease morbidity. There was conflicting evidence regarding effects on in-patient mortality and overall costs. Reported harms were alert fatigue, increased technology interaction time, creation of disruptive workarounds and new prescribing errors. Conclusion eHealth technologies in hospital settings appear to improve efficiency and appropriateness of care, prescribing safety and disease control. Effects on mortality, readmissions, total costs and patient and provider experience remain uncertain. What is known about the topic? Healthcare systems internationally are undertaking large-scale digitisation programs with hospitals being a major focus. Although predictive analyses suggest that eHealth technologies have the potential to markedly transform health care delivery, contemporary peer-reviewed research evidence detailing their benefits and harms is limited. What does this paper add? This narrative overview of 19 systematic reviews and two reviews of systematic reviews published over the past 5 years provides a summary of cumulative evidence of clinical and organisational effects of contemporary eHealth technologies in hospital practice. EMRs have the potential to increase accuracy and completeness of clinical information, reduce documentation time and enhance information transfer and organisational efficiency. CPOE appears to improve laboratory turnaround times and decrease resource utilisation. ePrescribing significantly reduces medication errors and adverse drug events. CDSS, especially those used at the point of care and integrated into workflows, attract the strongest evidence for substantially increasing clinician adherence to guidelines, appropriateness of disease and treatment monitoring and optimal medication use. Evidence of effects of eHealth technologies on discrete clinical outcomes, such as morbid events, mortality and readmissions, is currently limited and conflicting. What are the implications for practitioners? eHealth technologies confer benefits in improving quality and safety of care with little evidence of major hazards. Whether EMRs and CPOE can affect clinical outcomes or overall costs in the absence of auxiliary support systems, such as ePrescribing and CDSS, remains unclear. eHealth technologies are evolving rapidly and the evidence base used to inform clinician and managerial decisions to invest in these technologies must be updated continually. More rigorous field research using appropriate evaluation methods is needed to better define real-world benefits and harms. Customisation of eHealth applications to the context of patient-centred care and management of highly complex patients with multimorbidity will be an ongoing challenge.

Impact of Information Technology-Based Interventions for Type 2 Diabetes Mellitus on Glycemic Control: A Systematic Review and Meta-Analysis

BACKGROUND

Information technology-based interventions are increasingly being used to manage health care. However, there is conflicting evidence regarding whether these interventions improve outcomes in people with type 2 diabetes.

OBJECTIVE

The objective of this study was to conduct a systematic review and meta-analysis of clinical trials, assessing the impact of information technology on changes in the levels of hemoglobin A_1c (HbA_1c) and mapping the interventions with chronic care model (CCM) elements.

METHODS

Electronic databases PubMed and EMBASE were searched to identify relevant studies that were published up until July 2016, a method that was supplemented by identifying articles from the references of the articles already selected using the electronic search tools. The study search and selection were performed by independent reviewers. Of the 1082 articles retrieved, 32 trials (focusing on a total of 40,454 patients) were included. A random-effects model was applied to estimate the pooled results.

RESULTS

Information technology-based interventions were associated with a statistically significant reduction in HbA_1c levels (mean difference -0.33%, 95% CI -0.40 to -0.26, P<.001). Studies focusing on electronic self-management systems demonstrated the largest reduction in HbA_1c (0.50%), followed by those with electronic medical records (0.17%), an electronic decision support system (0.15%), and a diabetes registry (0.05%). In addition, the more CCM-incorporated the information technology-based interventions were, the more improvements there were in HbA_1c levels.

CONCLUSIONS

Information technology strategies combined with the other elements of chronic care models are associated with improved glycemic control in people with diabetes. No clinically relevant impact was observed on low-density lipoprotein levels and blood pressure, but there was evidence that the cost of care was lower.

Shift in responsibilities in diabetes care: the Nurse-Driven Diabetes In-Hospital Treatment protocol (N-DIABIT)

AIMS

To investigate the feasibility, safety and efficacy of the Nurse-Driven Diabetes In-Hospital Treatment protocol (N-DIABIT), which consists of nurse-driven correctional therapy, in addition to physician-guided basal therapy, and is carried out by trained ward nurses.

METHODS

Data on 210 patients with diabetes consecutively admitted in the 5-month period after the introduction of N-DIABIT (intervention group) were compared with the retrospectively collected data on 200 consecutive patients with diabetes admitted in the 5-month period before N-DIABIT was introduced (control group). Additional per-protocol analyses were performed in patients in whom mean patient-based protocol adherence was ≥ 70% (intervention subgroup, n = 173 vs. control subgroup, n = 196).

RESULTS

There was no difference between the intervention and the control group in mean blood glucose levels (8.9 ± 0.1 and 9.1 ± 0.2 mmol/l, respectively; P = 0.38), consecutive hyperglycaemic (blood glucose ≥ 10.0 mmol/l) episodes; P = 0.15), admission duration (P = 0.79), mean number of blood glucose measurements (P = 0.21) and incidence of severe hypoglycaemia (P = 0.29). Per-protocol analyses showed significant reductions in mean blood glucose levels and consecutive hypoglycaemia and hyperglycaemia in the intervention compared with the control group.

CONCLUSIONS

Implementation of N-DIABIT by trained ward nurses in non-intensive care unit diabetes care is feasible, safe and non-inferior to physician-driven care alone. High protocol adherence was associated with improved glycaemic control.

Standardized Glycemic Management with a Computerized Workflow and Decision Support System for Hospitalized Patients with Type 2 Diabetes on Different Wards

BACKGROUND

This study investigated the efficacy, safety, and usability of standardized glycemic management by a computerized decision support system for non-critically ill hospitalized patients with type 2 diabetes on four different wards.

MATERIALS AND METHODS

In this open, noncontrolled intervention study, glycemic management of 99 patients with type 2 diabetes (62% acute admissions; 41 females; age, 67±11 years; hemoglobin A1c, 65±21 mmol/mol; body mass index, 30.4±6.5 kg/m(2)) on clinical wards (Cardiology, Endocrinology, Nephrology, Plastic Surgery) of a tertiary-care hospital was guided by GlucoTab(®) (Joanneum Research GmbH [Graz, Austria] and Medical University of Graz [Graz, Austria]), a mobile decision support system providing automated workflow support and suggestions for insulin dosing to nurses and physicians.

RESULTS

Adherence to insulin dosing suggestions was high (96.5% bolus, 96.7% basal). The primary outcome measure, percentage of blood glucose (BG) measurements in the range of 70-140 mg/dL, occurred in 50.2±22.2% of all measurements. The overall mean BG level was 154±35 mg/dL. BG measurements in the ranges of 60-70 mg/dL, 40-60 mg/dL, and <40 mg/dL occurred in 1.4%, 0.5%, and 0.0% of all measurements, respectively. A regression analysis showed that acute admission to the Cardiology Ward (+30 mg/dL) and preexisting home insulin therapy (+26 mg/dL) had the strongest impact on mean BG. Acute admission to other wards had minor effects (+4 mg/dL). Ninety-one percent of the healthcare professionals felt confident with GlucoTab, and 89% believed in its practicality and 80% in its ability to prevent medication errors.

CONCLUSIONS

An efficacious, safe, and user-accepted implementation of GlucoTab was demonstrated. However, for optimized personalized patient care, further algorithm modifications are required.

Point-of-care blood glucose testing for diabetes care in hospitalized patients: an evidence-based review

Glycemic control in hospitalized patients with diabetes requires accurate near-patient glucose monitoring systems. In the past decade, point-of-care blood glucose monitoring devices have become the mainstay of near-patient glucose monitoring in hospitals across the world. In this article, we focus on its history, accuracy, clinical use, and cost-effectiveness. Point-of-care devices have evolved from 1.2 kg instruments with no informatics to handheld lightweight portable devices with advanced connectivity features. Their accuracy however remains a subject of debate, and new standards for their approval have now been issued by both the International Organization for Standardization and the Clinical and Laboratory Standards Institute. While their cost-effectiveness remains to be proved, their clinical value for managing inpatients with diabetes remains unchallenged. This evidence-based review provides an overall view of its use in the hospital setting.

Effectiveness of an educational intervention on the management of type 2 diabetic patients hospitalized in Internal Medicine: results from the FADOI-DIAMOND study

Appropriate management of hyperglycemia is crucial for patients with type 2 diabetes. Aim of the FADOI-DIAMOND study was to evaluate real-world management of type 2 diabetic patients hospitalized in Internal Medicine wards (IMW) and the effects of a standardized educational intervention for IMW staff. DIAMOND has been carried out in 53 Italian IMW, with two cross-sectional surveys interspersed with an educational program (PRE phase and POST phase). In PRE phase, each center reviewed the charts of the last 30 hospitalized patients with known type 2 diabetes. An educational program was conducted in each center by means of the "outreach visit," a face-to-face meeting between IMW staff and a trained external expert. Six months after, each center repeated the data collection (POST phase), specular to the PRE. A total of 3,167 patients were enrolled (1,588 PRE and 1,579 POST). From PRE phase to POST, patients with registered anthropometric data (54.1 vs. 74.9 %, p < 0.001) and in-hospital/recent measurement of glycated hemoglobin (48.2 vs. 61.4 %, p < 0.005) increased significantly. After educational program, more patients received insulin during hospitalization (68.3 vs. 63.6 %, p = 0.005). A more relevant variation in glycemia during hospitalization was observed in POST phase than PRE (-22.2 vs. -15.5 mg/dL, p < 0.001), without differences as for occurrence of hypoglycemia (12.3 vs. 11.9 %). A one-shot educational intervention led to persistent improvement in the management of hospitalized patients with type 2 diabetes and to significant better glycemic control. Further studies might evaluate the effectiveness of a more aggressive educational program, on both management and outcomes.

Temporal patterns of hypoglycaemia and burden of sulfonylurea-related hypoglycaemia in UK hospitals: a retrospective multicentre audit of hospitalised patients with diabetes

OBJECTIVES

To determine whether temporal patterns of hypoglycaemia exist in inpatients with diabetes 'at risk' of hypoglycaemia (those on insulin and/or sulfonylureas), and if so whether patterns differ between hospitals and between these treatments.

SETTING

Retrospective multicentre audit of inpatients with diabetes involving 11 acute UK National Health Service (NHS) trusts.

PARTICIPANTS

Capillary blood glucose readings of 3.9 mmol/L or less (hypoglycaemia) for all adult (≥18 years) inpatients with diabetes 'at risk' of hypoglycaemia were extracted from the Abbott PrecisionWeb Point-of-Care Data Management System over a 4-week period. Overall, 2521 readings of 3.9 mmol/L or less (hypoglycaemia) occurring in 866 participants between 1 June 2013 and 29 June 2013 were analysed.

RESULTS

The majority (65%) occurred between 21:00 and 08:59, a pattern common to all Trusts. This was more frequent in sulfonylurea-treated than insulin-treated participants (75.3% vs 59.3%, p=0.0001). Furthermore, hypoglycaemic readings were more frequent between 5:00 and 7:59 in sulfonylurea-treated than insulin-treated participants (46.7% vs 22.7% of readings for respective treatments, p=0.0001). Sulfonylureas accounted for 31.8% of all hypoglycaemic readings. As a group, sulfonylurea-treated participants were older (median age 78 vs 73 years, p=0.0001) and had lower glycated haemoglobin (median 56 (7.3%) vs 69 mmol/mol (8.5%), p=0.0001). Hypoglycaemic readings per participant were as frequent for sulfonylurea-treated participants as for insulin-treated participants (median=2 for both) as were the proportions in each group with ≥5 hypoglycaemic readings (17.3% vs 17.7%).

CONCLUSIONS

In all Trusts, hypoglycaemic readings were more frequent between 21:00 and 08:59 in 'at risk' inpatients with diabetes, with a greater frequency in the early morning period (5:00-7:59) in sulfonylurea-treated inpatients. This may have implications for the continuing use of sulfonylureas in the inpatient setting.

Glucose control in non-critically ill inpatients with diabetes: towards closed-loop

Inpatient glycaemic control remains an important issue due to the increasing number of patients with diabetes admitted to hospital. Morbidity and mortality in hospital are associated with poor glucose control, and cost of hospitalization is higher compared to non-diabetes patients. Guidelines for inpatient glycaemic control in the non-critical care setting have been published. Current recommendations include basal-bolus insulin therapy, regular glucose monitoring, as well as enhancing healthcare provider's role and knowledge. In spite of growing focus, implementation in practice is limited, mainly due to increasing workload burden on staff and fear of hypoglycaemia. Advances in healthcare technology may contribute to an improvement of inpatient diabetes care. Integration of glucose measurements with healthcare records and computerized glycaemic control protocols are currently being used in some institutions. Recent interests in continuous glucose monitoring have led to studies assessing its utilization in inpatients. Automation of glucose monitoring and insulin delivery may provide a safe and efficacious tool for hospital staff to manage inpatient hyperglycaemia, whilst reducing staff workload. This review summarizes the evidence on current approaches to managing inpatient glycaemic control; its utility and limitations. We conclude by discussing the evidence from feasibility studies to date, on the potential use of closed loop in the non-critical care setting and its implication for future studies.

Efficacy, usability and sequence of operations of a workflow-integrated algorithm for basal-bolus insulin therapy in hospitalized type 2 diabetes patients

AIMS

To evaluate glycaemic control and usability of a workflow-integrated algorithm for basal-bolus insulin therapy in a proof-of-concept study to develop a decision support system in hospitalized patients with type 2 diabetes.

METHODS

In this ward-controlled study, 74 type 2 diabetes patients (24 female, age 68 ± 11 years, HbA1c 8.7 ± 2.4% and body mass index 30 ± 7) were assigned to either algorithm-based treatment with a basal-bolus insulin therapy or to standard glycaemic management. Algorithm performance was assessed by continuous glucose monitoring and staff's adherence to algorithm-calculated insulin dose.

RESULTS

Average blood glucose levels (mmol/l) in the algorithm group were significantly reduced from 11.3 ± 3.6 (baseline) to 8.2 ± 1.8 (last 24 h) over a period of 7.5 ± 4.6 days (p < 0.001). The algorithm group had a significantly higher percentage of glucose levels in the ranges from 5.6 to 7.8 mmol/l (target range) and 3.9 to 10.0 mmol/l compared with the standard group (33 vs. 23% and 73 vs. 53%, both p < 0.001). Physicians' adherence to the algorithm-calculated total daily insulin dose was 95% and nurses' adherence to inject the algorithm-calculated basal and bolus insulin doses was high (98 and 93%, respectively). In the algorithm group, significantly more glucose values <3.9 mmol/l were detected in the afternoon relative to other times (p < 0.05), a finding mainly related to pronounced morning glucose excursions and requirements for corrective bolus insulin at lunch.

CONCLUSIONS

The workflow-integrated algorithm for basal-bolus therapy was effective in establishing glycaemic control and was well accepted by medical staff. Our findings support the implementation of the algorithm in an electronic decision support system.

Health information technology and hospital patient safety: a conceptual model to guide research

BACKGROUND

The literature indicates that health information technology (IT) use may lead to some gains in the quality and safety of care in some situations but provides little insight into this variability in the results that has been found. The inconsistent findings point to the need for a conceptual model that will guide research in sorting out the complex relationships between health IT and the quality and safety of care.

METHODS

A conceptual model was developed that describes how specific health IT functions could affect different types of inpatient safety errors and that include contextual factors that influence successful health IT implementation. The model was applied to a readily available patient safety measure and nationwide data (2009 AHA Annual Survey Information Technology Supplement and 2009 Healthcare Cost and Utilization Project State Inpatient Databases).

FINDINGS

The model was difficult to operationalize because (1) available health IT adoption data did not characterize health IT features and extent of usage, and (2) patient safety measures did not elucidate the process failures leading to safety-related outcomes. The sample patient safety measure--Postoperative Physiologic and Metabolic Derangement Rate--was not significantly related to self-reported health IT capabilities when adjusted for hospital structural characteristics.

CONCLUSION

These findings illustrate the critical need for collecting data that are germane to health IT and the possible mechanisms by which health IT may affect inpatient safety. Well-defined and sufficiently granular measures of provider's correct use of health IT functions, the contextual factors surrounding health IT use, and patient safety errors leading to health care-associated conditions are needed to illuminate the impact of health IT on patient safety.

Can computerized clinical decision support systems improve diabetes management? A systematic review and meta-analysis

AIMS

To systematically review randomized trials that assessed the effects of computerized clinical decision support systems in ambulatory diabetes management compared with a non-computerized clinical decision support system control.

METHODS

We included all diabetes trials from a comprehensive computerized clinical decision support system overview completed in January 2010, and searched EMBASE, MEDLINE, INSPEC/COMPENDEX and Evidence-Based Medicine Reviews (EBMR) from January 2010 to April 2012. Reference lists of related reviews, included articles and Clinicaltrials.gov were also searched. Randomized controlled trials of patients with diabetes in ambulatory care settings comparing a computerized clinical decision support system intervention with a non-computerized clinical decision support system control, measuring either a process of care or a patient outcome, were included. Screening of studies, data extraction, risk of bias and quality of evidence assessments were carried out independently by two reviewers, and discrepancies were resolved through consensus or third-party arbitration. Authors were contacted for any missing data.

RESULTS

Fifteen trials were included (13 from the previous review and two from the current search). Only one study was at low risk of bias, while the others were of moderate to high risk of bias because of methodological limitations. HbA1c (3 months' follow-up), quality of life and hospitalization (12 months' follow-up) were pooled and all favoured the computerized clinical decision support systems over the control, although none were statistically significant. Triglycerides and practitioner performance tended to favour computerized clinical decision support systems although results were too heterogeneous to pool.

CONCLUSIONS

Computerized clinical decision support systems in diabetes management may marginally improve clinical outcomes, but confidence in the evidence is low because of risk of bias, inconsistency and imprecision.

Failure to control hyperglycemia in noncritically ill diabetes patients despite standard glycemic management in a hospital setting

BACKGROUND

Successful control of hyperglycemia has been shown to improve outcomes for diabetes patients in a clinical setting. We assessed the quality of physician-based glycemic management in two general wards, considering the most recent recommendations for glycemic control for noncritically ill patients (<140 mg/dl for premeal glucose).

METHODS

Quality of glycemic management of 50 patients in two wards (endocrinology, cardiology) was assessed retrospectively by analyzing blood glucose (BG) levels, the glycemic management effort, and the online questionnaire.

RESULTS

Glycemic control was clearly above the recommended target (mean BG levels: endocrinology: 175 ± 62 mg/dl; cardiology: 186 ± 68 mg/dl). When comparing the first half with the second half of the hospital stay, we found no difference in glycemic control (endocrinology: 168 ± 32 vs 164 ± 42 mg/dl, P = .67; cardiology: 174 ± 36 mg/dl vs 170 ± 42 mg/dl, P =.51) and in insulin dose (endocrinology: 15 ± 14 IU vs 15 ± 13 IU per day, P = .87; cardiology: 27 ± 17 IU vs 27 ± 18 IU per day, P = .92), despite frequent BG measurements (endocrinology: 2.7 per day; cardiology: 3.2 per day). A lack of clearly defined BG targets was indicated in the questionnaire.

CONCLUSION

The recommended BG target range was not achieved in both wards. Analysis of routine glycemic management demonstrated considerable glycemic management effort, but also a lack of translation into adequate insulin therapy. Implementation of corrective measures, such as structured treatment protocols, is essential.