Greg Maynard

Project BOOST: effectiveness of a multihospital effort to reduce rehospitalization.

BACKGROUND Rehospitalization is a prominent target for healthcare quality improvement and performance-based reimbursement. The generalizability of existing evidence on best practices is unknown. OBJECTIVE To determine the effect of Project BOOST (Better Outcomes for Older adults through Safe Transitions) on rehospitalization rates and length of stay. DESIGN Semicontrolled pre-post study. SETTING/PARTICIPANTS Volunteer sample of 11 hospitals varying in geography, size, and academic affiliation. INTERVENTION Hospitals implemented Project BOOST-recommended tools supported by an external quality improvement physician mentor. METHODS Pre-post changes in readmission rates and length of stay within BOOST units, and between BOOST units and site-designated control units. RESULTS The average rate of 30-day rehospitalization in BOOST units was 14.7% prior to implementation and 12.7% 12 months later (P = 0.010), reflecting an absolute reduction of 2% and a relative reduction of 13.6%. Rehospitalization rates for matched control units were 14.0% in the preintervention period and 14.1% in the postintervention period (P = 0.831). The mean absolute reduction in readmission rates in BOOST units compared to control units was 2.0% (P = 0.054 for signed rank test comparing differences in readmission rate reduction in BOOST units compared to site-matched control units). CONCLUSIONS Participation in Project BOOST appeared to be associated with a decrease in readmission rates.

Improved inpatient use of basal insulin, reduced hypoglycemia, and improved glycemic control: effect of structured subcutaneous insulin orders and an insulin management algorithm.

BACKGROUND Structured subcutaneous insulin order sets and insulin protocols are widely advocated. The intervention effects are not well reported. OBJECTIVE Assess the impact of these interventions on insulin use patterns, hypoglycemia, and glycemic control. DESIGN Prospective observational. SETTING 400-bed academic center. PATIENTS Adult non-critical care inpatients with diabetes or hyperglycemia and point-of-care (POC) glucose testing. INTERVENTIONS Structured insulin orders, insulin management algorithm. MEASUREMENTS Percent of insulin orders with basal insulin. Percent uncontrolled patient-stays (day-weighted mean glucose >or=180 mg/dL) and uncontrolled patient-days (patient-day mean glucose >or=180 mg/dL). Percent of monitored patient-days and patient-stays with hypoglycemia (glucose <or=60 mg/dL) and severe hypoglycemia (glucose <or=40 mg/dL). RESULTS The percent sliding scale only insulin regimens decreased (72% versus 26% with structured insulin orders, P < 0.0001 chi square). The percent of uncontrolled patient-days was 37.8% versus 33.9% versus 30.1% (P < 0.005) (TP1-Baseline; TP2-Structured insulin orders; TP3-Orders plus Algorithm). Expressed as relative risk with 95% confidence interval (RR with CI), the RR of an uncontrolled patient-stay was reduced from baseline to 0.91 (CI 0.85-0.96) in TP2, and to 0.84 (CI 0.77-0.89) in TP3, with more marked effects in the secondary analysis limited to patients with at least 8 POC values. The percent of patient-days with hypoglycemia was 3.8%, 2.9%, and 2.6% in 3 time periods, representing a RR for hypoglycemic day in TP3:TP1 of 0.68 (CI 0.59-0.78). Similar reductions were seen in risk for hypoglycemic patient-stays. CONCLUSIONS Hypoglycemia and glycemic control can be improved simultaneously with structured insulin orders and management algorithms.

Designing and implementing effective venous thromboembolism prevention protocols: lessons from collaborative efforts

Hospital acquired venous thromboembolism (VTE) is a major source of morbidity and mortality, yet proven prevention measures are often underutilized. The lack of a validated VTE risk assessment model, difficulty integrating VTE risk assessment and prevention protocols into the routine process of care, and the lack of standardized metrics for VTE prophylaxis have all been barriers. Recently, a VTE risk assessment/prevention protocol has been validated, leading to portable strategies achieving breakthrough levels of adequate prophylaxis in a variety of inpatient settings. VTE prevention protocol design and implementation strategies have been collected in implementation guides available from the Society of Hospital Medicine and the Agency for Healthcare Research and Quality. These guides were the centerpieces of national collaborative efforts to improve VTE involving over 150 medical centers, honing the approach to accelerate improvement described in this article. Embedding a VTE prevention protocol into admission, transfer, and perioperative order sets is a key strategy. A VTE prevention protocol is defined as a VTE risk assessment with no more than three levels of risk, tightly linked to recommended prophylaxis for each level. A balance between the need to provide protocol guidance and the need for efficiency and ease-of-use by the clinician must be maintained. The power of this protocol driven approach is bolstered by a quality improvement framework, multidisciplinary teams, ongoing monitoring of the process, and real time identification and mitigation of non-adherents via a technique that measures progress and prompts concurrent intervention, an approach we call “measure-vention.”

Society of hospital medicine glycemic control task force summary: Practical recommendations for assessing the impact of glycemic control efforts

5 University of California San Diego, Division of Hospital Medicine, Department of Medicine, San Diego, California D ata collection, analysis, and presentation are key to the success of any hospital glycemic control initiative. Such efforts enable the management team to track improvements in processes and outcomes, make necessary changes to their quality improvement efforts, justify the provision of necessary time and resources, and share their results with others. Reliable metrics for assessing glycemic control and frequency of hypoglycemia are essential to accomplish these tasks and to assess whether interventions result in more benefit than harm. Hypoglycemia metrics must be especially convincing because fear of hypoglycemia remains a major source of clinical inertia, impeding efforts to improve glucose control. Currently, there are no official standards or guidelines for formulating metrics on the quality of inpatient glycemic control. This creates several problems. First, different metrics vary in their biases and in their responsiveness to change. Thus, use of a poor metric could lead to either a falsely positive or falsely negative impression that a quality improvement intervention is in fact improving glycemic control. Second, the proliferation of different measures and analytical plans in the research and quality improvement literature make it very difficult for hospitals to compare baseline performance, determine need for improvement, and understand which interventionsmay bemost effective. A related article in this supplement provides the rationale for improved inpatient glycemic control. That article argues that the current state of inpatient glycemic control, with the frequent occurrence of severe hyperglycemia and irrational insulin ordering, cannot be considered acceptable, especially given the large body of data (albeit largely observational) linking hyperglycemia to negative patient outcomes. However, regardless of whether one is an advocate or skeptic of tighter glucose control in the intensive care unit (ICU) and especially the non-ICU setting, there is no question that standardized, valid, and reliable metrics are needed to compare efforts to improve glycemic control, better understand whether such control actually improves patient care, and closely monitor patient safety. This article provides a summary of practical suggestions to assess glycemic control, insulin use patterns, and safety (hypoglycemia and severe hyperglycemia). In particular, we discuss the pros and cons of various measurement choices. We conclude with a tiered summary of recommendations for practical metrics No honoraria were paid to any authors for time and expertise spent on the writing of this article.

Subcutaneous insulin order sets and protocols: Effective design and implementation strategies†‡

6 Yale Diabetes Center, Yale New Haven Hospital, New Haven, Connecticut. I npatient glycemic control and hypoglycemia are issues with well deserved increased attention in recent years. Prominent guidelines and technical reviews have been published, and a recent, randomized controlled trial demonstrated the superiority of basal bolus insulin regimens compared to sliding-scale regimens. Effective glycemic control for inpatients has remained elusive in most medical centers. Recent reports detail clinical inertia and the continued widespread use of sliding-scale subcutaneous insulin regimens, as opposed to the anticipatory, physiologic ‘‘basal-nutrition-correction dose’’ insulin regimens endorsed by these reviews. Inpatient glycemic control faces a number of barriers, including fears of inducing hypoglycemia, uneven knowledge and training among staff, and competing institutional and patient priorities. These barriers occur in the background of an inherently complex inpatient environment that poses unique challenges in maintaining safe glycemic control. Patients frequently move across a variety of care teams and geographic locations during a single inpatient stay, giving rise to multiple opportunities for failed communication, incomplete handoffs, and inconsistent treatment. In addition, insulin requirements may change dramatically due to variations in the stress of illness, exposure tomedications that effect glucose levels, and varied forms of nutritional intake with frequent interruption. Although insulin is recognized as one of the medications most likely to be associated with adverse events in the hospital, many hospitals do not have protocols or order sets in place to standardize its use. A ‘‘Call to Action’’ consensus conference, hosted by the American Association of Clinical Endocrinologists (AACE) and the American Diabetes Association (ADA), brought together many thought leaders and organizations, including representation from the Society of Hospital Medicine (SHM), to address these barriers and to outline components necessary for successful implementation of a program to improve inpatient glycemic control in the face of these difficulties. Institutional insulin management protocols and standardized insulin order sets (supported by appropriate educational efforts) were identified as key interventions. It may be tempting to quickly deploy a generic insulin order set in an effort to improve care. This often results in mediocre results, due to inadequate incorporation of standardization and guidance into the order set and other documentation tools, and uneven use of the order set. The SHM Glycemic Control Task Force (GCTF) recommends the following steps for developing and implementing successful No honoraria were paid to any authors for time and expertise spent on the writing of this article.

Perioperative Care of the Geriatric Patient with Diabetes or Hyperglycemia

The incidence of diabetes in the geriatric population is increasing and the resulting co-morbidities have led to corresponding increases in hospital admissions and surgeries. The weight of the evidence and national guidelines should dissuade us from allowing uncontrolled hyperglycemia in the geriatric perioperative population, but the glycemic target should be modified upwards based on the individual patient characteristics, and in environments that do not have an established track record of reaching more aggressive targets safely. Insulin is the most effective and flexible regimen to achieve inpatient glycemic control, whether by infusion or by subcutaneous basal bolus regimens. Strategies for safe and effective dosing and adjustment of insulin regimens, and methods to avoid hypoglycemia in the perioperative period are outlined. Finally, discharge planning should take into consideration a patients HbA1c, preoperative glycemic control, inpatient glycemic regimen and control, financial and physical limitations, social support, co-morbid medical conditions, episodes of hypoglycemia, and overall prognosis to create an individualized safe and effective medication regimen for optimal glycemic control at home.

IMPACT OF A HYPOGLYCEMIA REDUCTION BUNDLE AND A SYSTEMS APPROACH TO INPATIENT GLYCEMIC MANAGEMENT

OBJECTIVE Uncontrolled hyperglycemia and iatrogenic hypoglycemia represent common and frequently preventable quality and safety issues. We sought to demonstrate the effectiveness of a hypoglycemia reduction bundle, proactive surveillance of glycemic outliers, and an interdisciplinary data-driven approach to glycemic management. METHODS POPULATION all hospitalized adult non-intensive care unit (non-ICU) patients with hyperglycemia and/or a diagnosis of diabetes admitted to our 550-bed academic center across 5 calendar years (CYs). INTERVENTIONS hypoglycemia reduction bundle targeting most common remediable contributors to iatrogenic hypoglycemia; clinical decision support in standardized order sets and glucose management pages; measure-vention (daily measurement of glycemic outliers with concurrent intervention by the inpatient diabetes team); educational programs. MEASURES AND ANALYSIS Pearson chi-square value with relative risks (RRs) and 95% confidence intervals (CIs) were calculated to compare glycemic control, hypoglycemia, and hypoglycemia management parameters across the baseline time period (TP1, CY 2009-2010), transitional (TP2, CY 2011-2012), and mature postintervention phase (TP3, CY 2013). Hypoglycemia defined as blood glucose <70 mg/dL, severe hypoglycemia as <40 mg/dL, and severe hyperglycemia >299 mg/dL. RESULTS A total of 22,990 non-ICU patients, representing 94,900 patient-days of observation were included over the 5-year study. The RR TP3:TP1 for glycemic excursions was reduced significantly: hypoglycemic stay, 0.71 (95% CI, 0.65 to 0.79); severe hypoglycemic stay, 0.44 (95% CI, 0.34 to 0.58); recurrent hypoglycemic day during stay, 0.78 (95% CI, 0.64 to 0.94); severe hypoglycemic day, 0.48 (95% CI, 0.37 to 0.62); severe hyperglycemic day (>299 mg/dL), 0.76 (95% CI, 0.73 to 0.80). CONCLUSION Hyperglycemia and hypoglycemia event rates were both improved, with the most marked effect on severe hypoglycemic events. Most of these interventions should be portable to other hospitals.

Design and Implementation of a Web-Based Reporting and Benchmarking Center for Inpatient Glucometrics

Background: Insulin is a top source of adverse drug events in the hospital, and glycemic control is a focus of improvement efforts across the country. Yet, the majority of hospitals have no data to gauge their performance on glycemic control, hypoglycemia rates, or hypoglycemic management. Current tools to outsource glucometrics reports are limited in availability or function. Methods: Society of Hospital Medicine (SHM) faculty designed and implemented a web-based data and reporting center that calculates glucometrics on blood glucose data files securely uploaded by users. Unit labels, care type (critical care, non–critical care), and unit type (eg, medical, surgical, mixed, pediatrics) are defined on upload allowing for robust, flexible reporting. Reports for any date range, care type, unit type, or any combination of units are available on demand for review or downloading into a variety of file formats. Four reports with supporting graphics depict glycemic control, hypoglycemia, and hypoglycemia management by patient day or patient stay. Benchmarking and performance ranking reports are generated periodically for all hospitals in the database. Results: In all, 76 hospitals have uploaded at least 12 months of data for non–critical care areas and 67 sites have uploaded critical care data. Critical care benchmarking reveals wide variability in performance. Some hospitals achieve top quartile performance in both glycemic control and hypoglycemia parameters. Conclusions: This new web-based glucometrics data and reporting tool allows hospitals to track their performance with a flexible reporting system, and provides them with external benchmarking. Tools like this help to establish standardized glucometrics and performance standards.

Best Practices for Interdisciplinary Care Management by Hospital Glycemic Teams: Results of a Society of Hospital Medicine Survey Among 19 U.S. Hospitals

Objective. The Society for Hospital Medicine (SHM) conducted a survey of U.S. hospital systems to determine how nonphysician providers (NPPs) are utilized in interdisciplinary glucose management teams. Methods. An online survey grouped 50 questions into broad categories related to team functions. Queries addressed strategies that had proven successful, as well as challenges encountered. Fifty surveys were electronically distributed with an invitation to respond. A subset of seven respondents identified as having active glycemic committees that met at least every other month also participated in an in-depth telephone interview conducted by an SHM Glycemic Advisory Panel physician and NPP to obtain further details. The survey and interviews were conducted from May to July 2012. Results. Nineteen hospital/hospital system teams completed the survey (38% response rate). Most of the teams (52%) had existed for 1–5 years and served 90–100% of noncritical care, medical critical care, and surgical units. All of the glycemic control teams were supported by the use of protocols for insulin infusion, basal-bolus subcutaneous insulin orders, and hypoglycemia management. However, > 20% did not have protocols for discontinuation of oral hypoglycemic agents on admission or for transition from intravenous to subcutaneous insulin infusion. About 30% lacked protocols assessing A1C during the admission or providing guidance for insulin pump management. One-third reported that glycemic triggers led to preauthorized consultation or assumption of care for hyperglycemia. Institutional knowledge assessment programs were common for nurses (85%); intermediate for pharmacists, nutritionists, residents, and students (40–45%); and uncommon for fellows (25%) and attending physicians (20%). Many institutions were not monitoring appropriate use of insulin, oral agents, or insulin protocol utilization. Although the majority of teams had a process in place for post-discharge referrals and specific written instructions were provided, only one-fourth were supported with written protocols to standardize medication, education, equipment, and follow-up instructions. Conclusion. Inpatient glycemic control teams with NPPs often function in environments without a full set of measurement, education, standardization, transition, and order tools. Executive hospital leaders, community partners, and the glycemic control teams themselves need to address these deficiencies to optimize team effectiveness.

Venous thromboembolism prevention guidelines for medical inpatients: mind the (implementation) gap.

BACKGROUND Hospital-associated nonsurgical venous thromboembolism (VTE) is an important problem addressed by new guidelines from the American College of Physicians (ACP) and American College of Chest Physicians (AT9). METHODS Narrative review and critique. RESULTS Both guidelines discount asymptomatic VTE outcomes and caution against overprophylaxis, but have different methodologies and estimates of risk/benefit. Guideline complexity and lack of consensus on VTE risk assessment contribute to an implementation gap. Methods to estimate prophylaxis benefit have significant limitations because major trials included mostly screening-detected events. AT9 relies on a single Italian cohort study to conclude that those with a Padua score ≥4 have a very high VTE risk, whereas patients with a score <4 (60% of patients) have a very small risk. However, the cohort population has less comorbidity than US inpatients, and over 1% of patients with a score of 3 suffered pulmonary emboli. The ACP guideline does not endorse any risk-assessment model. AT9 includes the Padua model and Caprini point-based system for nonsurgical inpatients and surgical inpatients, respectively, but there is no evidence they are more effective than simpler risk-assessment models. CONCLUSIONS New VTE prevention guidelines provide varied guidance on important issues including risk assessment. If Padua is used, a threshold of 3, as well as 4, should be considered. Simpler VTE risk-assessment models may be superior to complicated point-based models in environments without sophisticated clinical decision support.