Poor inpatient glycemic management is associated with increased lengths of stay and in-hospital morbidity and mortality. Improving inpatient glycemic outcomes can be difficult because there are no standardized benchmarks, and many hospitals lack the capacity to electronically extract and analyze glucose data. The Centers for Medicare & Medicaid Services recently proposed new electronic clinical quality measures to be incorporated into its mandatory Hospital Inpatient Quality Reporting Program. Among these measures is an assessment of hospital harm from severe hypoglycemia and severe hyperglycemia. Hospitals must be ready to collect the necessary data for these new measures by January 2023. The new measures could bring welcome attention to the need to implement guideline-based inpatient glycemic management. However, some hospitals that serve high-risk populations may be at risk for losing funding if they are unable to comply.

About 11.3% of the U.S. population has diabetes, and nearly 38% has prediabetes, according to the Centers for Disease Control and Prevention (1). Approximately 25% of all hospital inpatient days are incurred by people with a diagnosis of diabetes (2). One-third of these patients have another hospitalization within 1 year, with this subset accounting for >50% of total hospitalizations and hospital costs of patients with diabetes (3).

A person’s outpatient diabetes regimen may be inadequate to manage blood glucose in the face of acute illness and changes in eating behaviors while in the hospital. Insulin remains the preferred treatment for hyperglycemia in hospitalized patients (4). Importantly, insulin is considered a high-alert medication by the Institute for Safe Medication Practices (5), as it bears a heightened risk of causing significant patient harm when used erroneously. Insulin-related medication errors are among the most likely to cause harm (6).

Despite the existence of published evidence-based guidelines for improving glycemic outcomes of hospitalized patients with diabetes (4), these are not implemented consistently for a variety of reasons. Most people with diabetes are hospitalized for reasons other than glucose management (7). As a result of competing priorities and a lack of sufficient training in recognizing poor glycemic management, care providers frequently exhibit therapeutic inertia with regard to adjusting insulin and other antidiabetic medications during a patient’s hospital stay. This inaction leads to significant glucose variability that can affect health outcomes. If changes are made to a patient’s diabetes management, these changes typically are more reactive (e.g., adding correctional insulin) instead of the proactive approach advocated in American Diabetes Association (ADA) and Endocrine Society guidelines, which involves the use of a basal-bolus insulin regimen (4,8,9). On the other hand, in the intensive care setting, there is still widespread use of outdated paper protocols that are neither provider friendly nor personalized for patients and thus can lead to errors and suboptimal glycemic outcomes.

Inpatient hyperglycemia is associated with numerous poor outcomes, including increased rates of infection, longer hospital lengths of stay, higher risks of intensive care unit admission, and increased in-hospital mortality (1013). Hypoglycemia is similarly associated with adverse patient outcomes and increased mortality (14,15). Hyperglycemia and hypoglycemia, collectively called “dysglycemia,” frequently go hand in hand, as efforts to correct low glucose can lead to high glucose and vice versa.

There is no single laboratory test to assess overall glycemic outcomes in the inpatient setting, in contrast to the outpatient setting, for which A1C is considered an acceptable performance standard. Instead, hospitals must rely on multiple point-of-care and serum glucose measurements obtained over a variety of nutritional states during a patient’s hospital stay. At Johns Hopkins Medicine, we have developed our own electronic health record (EHR) system dashboard for glucose performance metrics (also called “glucometrics”) to enable ongoing surveillance and benchmarking (16,17). In 2008, up to 59% of hospitals surveyed indicated that they did not have an automated way of extracting and analyzing glucose data (18), although this has likely improved somewhat in the years since then. The same study also noted that there was no standardized definition for the threshold of hypoglycemia that should be considered clinically relevant among hospitalized patients. The ADA’s Standards of Medical Care in Diabetes has adopted three levels of hypoglycemia (19), but it remains unclear whether these levels transfer well to inpatient care. Thus, even when hospitals can extract and analyze glucose data, there are no standardized glucometrics that would enable institutions to quantify their baseline performance and track the success of various quality improvement (QI) initiatives over time (2023). This also means that it is difficult for monitoring agencies to compare hospitals’ glycemic outcomes against each other. The Society of Hospital Medicine is working toward developing a way for hospitals to benchmark their performance in comparison with other institutions through its Glycemic Control Electronic Quality Improvement Programs (eQUIPS) (24). Additionally, the Centers for Medicare & Medicaid Services (CMS) recently proposed new electronic clinical quality measures (eCQMs) for dysglycemia to be incorporated into its mandatory quality reporting program for hospitals. In the remainder of this article, we describe and answer some common questions about the CMS eCQMs and what hospitals can expect as they are implemented.

The CMS is the largest funder of health care in the United States. Its payments accounted for 66% of all hospital payments in 2017 (25), with the remainder largely coming from private insurance companies. CMS plays a central role in developing measures that can be used to support health care delivery to its beneficiaries and beyond. Through a pay-for-reporting program known as the Hospital Inpatient Quality Reporting (IQR) Program, CMS mandates that certain quality measures be assessed and reported. Hospitals could be subject to 25% reduction in annual payment update increase for failure to report. The data collected through the IQR program is publicly available to consumers and health care providers online (26) to facilitate more informed health care decisions. Data on quality measures are collected or reported in a variety of ways, such as claims, assessment instruments, chart abstractions, and registries. Since 2016, hospitals have been required to report on some eCQM data as a portion of the IQR program. These metrics ideally are based on evidence that failure to meet identified benchmarks is likely to result in suboptimal clinical outcomes. The goal of these measures is to shift health care in the United States from a fee-for-service system to a value-based system.

In its annual update for fiscal year 2022, released in August 2021 (27), CMS included for the first time “hospital harm” glycemic measures in a list of 11 eCQMs. The definitions for these glycemic metrics are provided in Table 1 (28,29). Hospitals must report on three self-selected eCQMs in addition to a Safe Use of Opioids measure. Hospitals will need to start collecting data on these glycemic measures in January 2023.

Table 1

CMS Hospital Harm eCQMs for Severe Hypoglycemia and Hyperglycemia

eCQM for Severe HypoglycemiaeCQM for Severe Hyperglycemia
Brief description The proportion of inpatient hospitalizations for patients who are administered at least one hypoglycemic medication during the encounter, in which patients suffer the harm of a severe hypoglycemic event The proportion of inpatient hospital days with the harm of a hyperglycemic event relative to the total of qualifying inpatient hospital days 
Intention To measure iatrogenic events triggered by the incorrect use of insulin or another hypoglycemic medication, as severe hypoglycemia is considered a largely preventable adverse event To measure untreated and prolonged hyperglycemia that could inhibit a patient’s ability to recover and thus to help hospitals prioritize early, evidence-based treatment of hyperglycemia 
Numerator The number of inpatient hospitalizations that include 1) a severe hypoglycemic event during the encounter, defined as a laboratory or point-of-care blood glucose test result <40 mg/dL, and 2) a hypoglycemic medication administered within 24 hours before the start of the severe hypoglycemic event and during the encounter; only the first qualifying severe hypoglycemic event is counted in the numerator, and only one severe hypoglycemic event is counted per encounter The number of inpatient hospitalizations with a hyperglycemic event within the first 10 days of the encounter. A hyperglycemic event is defined as 1) a day with at least one blood glucose value >300 mg/dL or 2) a day during which blood glucose was not measured but that was preceded by two consecutive days during which at least one glucose value per day was ≥200 mg/dL 
Denominator The number of inpatient hospitalizations during which the patient received at least one hypoglycemic medication during the encounter; this includes administration of hypoglycemic medications in the emergency department or when the patient is in observation status at the start of a hospitalization The number of inpatient hospitalizations during which the patient has at least one of the following: a diagnosis of diabetes made before or during the encounter; administration of at least one dose of insulin or any hypoglycemic medication during the encounter; or presence of at last one blood glucose value ≥200 mg/dL at any time during the encounter; this includes inpatient hospitalizations that began in the emergency department or with the patient in observational status 
Exclusions 1. Events involving patients who are <18 years of age
2. Patients who have a blood glucose value <40 mg/dL with a subsequent retest value >80 mg/dL within 5 minutes (i.e., possibly spurious readings) 
1. Events involving patients who are <18 years of age
2. Patients who have a hyperglycemic glucose value in the first 24 hours after admission (allowing for correction of hyperglycemia present at admission) or in the last partial days before discharge (may not be able to measure blood glucose the last day if it is only a few hours long) 
eCQM for Severe HypoglycemiaeCQM for Severe Hyperglycemia
Brief description The proportion of inpatient hospitalizations for patients who are administered at least one hypoglycemic medication during the encounter, in which patients suffer the harm of a severe hypoglycemic event The proportion of inpatient hospital days with the harm of a hyperglycemic event relative to the total of qualifying inpatient hospital days 
Intention To measure iatrogenic events triggered by the incorrect use of insulin or another hypoglycemic medication, as severe hypoglycemia is considered a largely preventable adverse event To measure untreated and prolonged hyperglycemia that could inhibit a patient’s ability to recover and thus to help hospitals prioritize early, evidence-based treatment of hyperglycemia 
Numerator The number of inpatient hospitalizations that include 1) a severe hypoglycemic event during the encounter, defined as a laboratory or point-of-care blood glucose test result <40 mg/dL, and 2) a hypoglycemic medication administered within 24 hours before the start of the severe hypoglycemic event and during the encounter; only the first qualifying severe hypoglycemic event is counted in the numerator, and only one severe hypoglycemic event is counted per encounter The number of inpatient hospitalizations with a hyperglycemic event within the first 10 days of the encounter. A hyperglycemic event is defined as 1) a day with at least one blood glucose value >300 mg/dL or 2) a day during which blood glucose was not measured but that was preceded by two consecutive days during which at least one glucose value per day was ≥200 mg/dL 
Denominator The number of inpatient hospitalizations during which the patient received at least one hypoglycemic medication during the encounter; this includes administration of hypoglycemic medications in the emergency department or when the patient is in observation status at the start of a hospitalization The number of inpatient hospitalizations during which the patient has at least one of the following: a diagnosis of diabetes made before or during the encounter; administration of at least one dose of insulin or any hypoglycemic medication during the encounter; or presence of at last one blood glucose value ≥200 mg/dL at any time during the encounter; this includes inpatient hospitalizations that began in the emergency department or with the patient in observational status 
Exclusions 1. Events involving patients who are <18 years of age
2. Patients who have a blood glucose value <40 mg/dL with a subsequent retest value >80 mg/dL within 5 minutes (i.e., possibly spurious readings) 
1. Events involving patients who are <18 years of age
2. Patients who have a hyperglycemic glucose value in the first 24 hours after admission (allowing for correction of hyperglycemia present at admission) or in the last partial days before discharge (may not be able to measure blood glucose the last day if it is only a few hours long) 

Having the ability to automatically extract data on eCQMs from an EHR system instead of manually reviewing and summarizing population-level glucose data would help to reduce the reporting burden by requiring fewer human resources. Overall, eCQMs are considered an efficient mechanism for extracting quality information from EHR systems and do not require sampling, as the full population is included in the data.

However, despite the benefits of eCQMs, they do require structured data entry, and this burden can fall on already overworked health care providers and information technology (IT) experts. Therefore, it is essential for hospital administrators to partner with members of the care team such as physicians, nurses, and pharmacists while in the development phase to incorporate these into the daily workflows of clinicians. For example, providers will be tasked with accurately documenting whether a patient has a history of diabetes and whether a blood glucose value is erroneous and needs confirmation with prompt retesting. Additionally, an EHR glucose management tab or prompts for various thresholds of hypoglycemia and hyperglycemia could allow providers to act on dysglycemia more efficiently, before a patient has a true blood glucose level <40 or >300 mg/dL.

Adoption of the CMS eCQMs has been slower than anticipated, in part because of a lack of local and federal IT resources and sluggish adoption of EHR systems in general across the country. More recent challenges have included competing priorities during the coronavirus disease 2019 pandemic. The software design for these platforms also requires some flexibility, as the eCQMs are updated annually.

Although these new glycemic eCQMs (i.e., hypoglycemia and hyperglycemia) are intended to be used simultaneously as a balance to minimize the potential for blood glucose variability, hospitals currently can choose to include one, both, or neither in their reporting.

Long-Term Goals of Glycemic eCQMs

Hospitals may not know what they don’t know; thus, we speculate that the glycemic eCQMs were developed to increase awareness of the importance of inpatient glycemic management and thus to reduce hospital harms. Additionally, establishing benchmarks through the implementation of eCQMs will help hospitals measure their status quo and assess the impact of interventions they may implement to improve inpatient glycemic management. The hope is that these metrics will allow for more meaningful analysis of glycemic data to support QI and real-time clinical decision-making. Initiatives to reduce dysglycemia rates will not only prevent symptomatic hyperglycemia and hypoglycemia but may also help to reduce mortality rates and infection risks, decrease lengths of stay, and cut costs (10,3034).

Necessary Preparations for Glycemic eCQMs

Hospital administrators should elicit the help of diabetes experts such as endocrinologists and diabetes care and education specialists who are experienced in inpatient diabetes management and form an interdisciplinary inpatient glycemic management team if none exists. For example, in all three community hospitals within Johns Hopkins Medicine, board-certified inpatient endocrinologists (also called “endocrine hospitalists”) oversee all diabetes-related QI initiatives (35). The hospital’s chief quality officer and chief medical officer are frequently tasked by the CMS and other accrediting organizations with reporting on the quality measures. It will also be necessary to collaborate with data analysts or IT officers, as many hospitals currently do not have a system in place to extract and analyze electronic glucose data. Within the coming months, hospitals that intend to include the new glycemic eCQMs in their CMS reporting will have to work with their EHR analysts to develop such automated systems to get ready for data collection starting in January 2023 (Figure 1). Several third-party software developers may assist with this task by providing blood glucose data analytics as part of their service. Examples include Glytec (Glucommander), Monarch Medical Technologies (EndoTool), and Medical Decision Network (GlucoStabilizer) (3638).

FIGURE 1

Hypothetical timeline of CMS hospital harms measures for severe hypoglycemia and hyperglycemia beginning with the August 2021 final rule announcement for fiscal year 2022. Subsequent steps include the development of automated data extraction and analysis capabilities in line with the proposed glycemic eCQMs (September 2021 to December 2022), implementation of eCQM data collection (January to December 2023), internal review of glucometrics to decide which eCQMs to report (February 2024), public availability of reported data (October 2024), and possible financial penalties for hospitals that fail to meet IQR reporting requirements (2025).

FIGURE 1

Hypothetical timeline of CMS hospital harms measures for severe hypoglycemia and hyperglycemia beginning with the August 2021 final rule announcement for fiscal year 2022. Subsequent steps include the development of automated data extraction and analysis capabilities in line with the proposed glycemic eCQMs (September 2021 to December 2022), implementation of eCQM data collection (January to December 2023), internal review of glucometrics to decide which eCQMs to report (February 2024), public availability of reported data (October 2024), and possible financial penalties for hospitals that fail to meet IQR reporting requirements (2025).

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Finally, once an interdisciplinary team is formed (at Johns Hopkins Medicine, we call ours the Glucose Steering Committee) and a glucometrics dashboard is developed, glycemic data should be tracked monthly or at least quarterly. The expected deadline for reporting such data to CMS is January 2024. Before this, hospitals will have to review the data themselves and decide whether to report on these metrics as opposed to the other eCQMs (e.g., venous thromboembolism prophylaxis or discharge with antithrombotic medication for patients with ischemic stroke). Even if the glycemic metrics are not reported, the hope is that these data will raise awareness of the need for targeted clinician education and possible internal process changes within hospital systems.

Possible Future Directions

As noted above, these measures are currently self-selected but may become mandatory eCQMs in the future. Based on our experience with similar metrics, we speculate that hospitals should be mindful that, although these metrics are currently structured as pay-for-reporting measures, they could soon become pay-for-performance measures. Thus, having a system to compare performance with similar hospitals will be of great value in determining individual hospital glycemic goals.

Additional measures may be around the corner such as metrics for recurrent hypoglycemia and timeliness of treatment during an admission to reduce therapeutic inertia that leads to dysglycemia. Measures of glycemic time in range can be the next step in promoting a well-rounded approach to reducing both hypoglycemia and hyperglycemia simultaneously (4,9). Most hospitals target a blood glucose range of 100–180 or 140–180 mg/dL before meals depending on clinical status and avoid blood glucose levels <100 mg/dL to reduce the risk of hypoglycemia.

National programs that address the care of patients with sepsis, acute myocardial infarction, and heart failure have led to significant improvements in evidence-based care for those conditions. Similar quality reporting measures introduced in the past to standardize the use of venous thromboembolism prophylaxis in hospitalized patients led to the development of anticoagulation task forces in many hospitals. The hope is that the need to obtain and report glycemic eCQMs will inspire hospital systems to develop similar task forces and action plans.

Although many of the past programs mentioned above originated from the CMS, commercial insurers have also moved toward performance-based payment models. Proponents of the value-based model argue that having benchmarks for performance helps to prioritize quality over quantity of care and helps encourage best clinical practices. Using metrics that are publicly reported helps to improve transparency and provides an incentive for organizations to strengthen their reputation in the public domain. This practice encourages accountability and competition through reporting systems. As previously mentioned, the pay-for-reporting system for glycemic eCQMs will likely evolve into a pay-for-performance model in the future. Similar pay-for-performance models have been successful in reducing undesirable outcomes such as 30-day hospital readmissions (39).

Concerns about these systems stem from potential harm it could pose to hospitals that serve socioeconomically disadvantaged populations. Hospitals that treat a larger volume of low-income, high-risk patients who struggle to engage with the health care system may not perform well on pay-for-performance measures (40). Furthermore, administrative costs to develop automated systems to gather and verify necessary data may be substantial.

These measures in their current form may be helpful in bringing attention to the need to improve glycemic outcomes but do not directly inform us regarding how to bring about these improvements. They also lump data from noncritical and critical care units together, making in-depth analysis to identify areas with more room for improvement difficult. We also do not have any measures for more commonly encountered levels of hypoglycemia (i.e., <70 and <54 mg/dL). Thus, protocols developed to prevent hypoglycemia may only be aimed at preventing the most severe hypoglycemic events of <40 mg/dL, even though patients may have adverse events at higher blood glucose levels.

These measures are an evidence-based approach to improving patient outcomes and standardizing the quality of health care provided across hospitals and populations. The latter could be achieved by adjusting benchmarks for population characteristics (e.g., age, sex, and insurance status) (41). This approach would also allow hospitals with high overall levels of performance to identify and address gaps in care for certain subpopulations. When developing such systems, health care organizations should engage with CMS to ensure that there are safeguards in place to address social determinants of health.

The key to success with glycemic eCQMs will depend on how hospitals choose to partner with diabetes experts, quality and IT officers, physicians, advanced practice providers, pharmacists, and nurses in incorporating these new metrics into their QI initiatives. This strategy will involve careful development of algorithms that are user-friendly, thoughtful, and efficient in the way they analyze glycemic data. Ultimately, achieving high performance ratings on these metrics would help hospitals achieve or maintain a reputation for being a high-reliability health care organization.

Duality of Interest

No potential conflicts of interest relevant to this article were reported.

Author Contributions

S.A.K. reviewed the literature and prepared the manuscript. M.Z. supervised preparation of the manuscript and was involved in final revisions. S.A.K. is the guarantor of this work and, as such, takes responsibility for the integrity of the data presented and the literature review.

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