Building the Business Case for Your Inpatient Glycemic Control Efforts

Michelle F. Magee, MD^1,2,3 and Adam Beck, MHS, FABC^1
1MedStar Research Institute, Washington, DC; ²MedStar Diabetes Institute, Washington, DC;
³Georgetown University School of Medicine, Washington, DC;

Introduction

Implementation of targeted inpatient glycemic control by a multidisciplinary team in the hospital is a time- and labor-intensive undertaking. A variety of models may be applied to provide justification for financial support for such initiatives. These models may be grouped as (A) those in which the hospital will provide support for the glycemic management team based on improved docu­men­ta­tion opportunities, reduction in length of stay and readmission rates, and improved resource utili­zation; and (B) those that will be self-supporting through billing and reimbursement of phy­si­cian and/or allied health care provider clinical diabetes case management consultative services (Table 1). In practice, it may be necessary to use a combination of these strategies.

To make the case to a hospital administration about why it should provide financial support for glycemic control programs, it is necessary to develop a business plan that justifies return on investment. The discussion following is based on an amalgam of information from the published literature, personal communication with hospital glycemic control “champions,” and the authors’ experience developing, implementing, and assessing outcomes for a glycemic control task force charged with improving glucose control in the seven-hospital MedStar Health system from which more than 45,000 patients are discharged annually with a diagnosis of diabetes.

The underlying principles for making the business case for support of a glycemic control team are the same as those applied in other hospital program business plans. Although the clinical argument for a new process, resource, or staff member may be sound, it is important to understand that a formal review group is composed of individuals who will be analyzing the business plan from nonclinical perspectives as well as, typically, the chief financial officer or another representative from finance and an operations representative, often the chief operating officer. These individuals along with departmental heads and administrators will be testing the business plan not only for its clinical impact but also for its fiscal and operational feasibility. For example, if a business plan for improved glycemic control includes the addition of a nurse-practitioner to the glycemic control team, a question from finance will be why a nurse-practitioner is required versus a lower-level, thus lower-cost, clinician. Another question from finance in this scenario will be about the true ability of nurse-practitioners to bill for services and for the hospital to collect for these services. Operations will review how nurse-practitioners would fit into current systems and processes and how they would be teamed with physicians. Business plans regarding staffing should always include reporting and oversight responsibility and because space is always at a premium, any applicable office or workstation logistics. It is important to involve operations and finance representatives early and often while developing the business plan in order to obtain their support and avoid any unforeseen questions that may weaken the request.

The following discussion focuses on those considerations in the process specific to inpatients with diabetes and/or hyperglycemia. In applying a model for justifying financial support for the hospital glycemic control team, it is important to emphasize that each model must be assessed, as mentioned above, in collaboration with the individual hospital or hospital system administration, finance and resources management departments and other key stakeholders, such as coding and reimbursement specialists, for the plan developed to be realistic and relevant to your specific locale and of value to your institution or institutions. The information provided is intended to be practical guidance to assist the hospital glycemic control team in justifying financial support for its initiative.

A. Hospital-supported models

The business case for hospital support for the glycemic control team is based on: opportunities related to improving documentation and coding accuracy, reduction in length of stay and readmission rates, and optimization of resource utilization through reduction in morbidity and mortality (cost aversion).

To understand opportunities for improved revenue through assuring accuracy of documentation and coding for patients with hospital hyperglycemia it is necessary to understand how hospitals are reimbursed for their inpatients. Whether or not a patient has Medicare, Medicaid, or another commercial payer, the principal and all secondary diagnoses and procedures must be accurately and appropriately documented in the patient medical record. Hospital coding personnel use this documentation according to strict guidelines and ICD-9-CM nomenclature to establish principal and secondary diagnoses and procedures for each inpatient case. The ICD-9-CM is a numerical coding scheme of more than 13,000 diagnoses and 5000 procedures. The principal and secondary diagnoses and procedures are grouped into a diagnosis-related group (DRG) code. In most states, Medicare reimburses hospitals according to the DRG code assigned to each inpatient case. The DRG payment for a Medicare patient is determined by multiplying the relative weight of the DRG by the hospital’s blended rate: DRG PAYMENT = WEIGHT x RATE. Each hospital’s payment rate is defined by federal regulations and is updated annually to reflect inflation, technical adjustments, and budgetary constraints. Rate calculations for large urban hospitals are separate from those for other hospitals. Technical adjustments are also made for local wage variations, teaching hospitals, and hospitals with a disproportionate share of financially indigent patients.1

The average DRG weight for all of a hospital’s inpatient volume is referred to as the case-mix index (CMI), which indicates the relative severity of a patient population and is directly proportional to DRG payments. This index is valuable when making comparisons between hospitals or patient groups.

Determination of allowable charges for inpatient care varies by location and payer. The state of Maryland, for example, regulates hospital inpatient charges for all patients on the basis of the CMI of each hospital. In this reimbursement system, a hospital must ensure that its total average charge per case is on a par with its reported CMI. As noted previously, in most other states, Medicare reimburses a set flat rate for each DRG. For other payers, the reimbursement may also be based on a set DRG payment or a percentage of charges based on contractual stipulations. The business case will be based on the individual hospital’s allowed charges.

Within the system used for hospital reimbursement, strategies used to justify hospital support for the glycemic control team include those based on: opportunities for improved accuracy of documentation and coding, reduction in length of stay, and resource utilization.

1. Documentation opportunities
The hospital glycemic control team may assess the potential for optimizing reimbursement through improved accuracy of physician documentation and coding. The three areas in which opportunities exist in this regard are: documentation of diabetes as controlled or uncontrolled, identification of previously unrecognized diabetes, and accuracy in the specification of diabetes complications. Each of these opportunities must be assessed in collaboration with the coding and reimbursement and finance departments of a given hospital or hospital system.

To mine data specific to each of these areas, it is necessary to clearly define the criteria by which they will be identified. This will enable collection of specific data on which the business case can be built.

1. Accuracy of designation of diabetes as controlled or uncontrolled

The glycemic control team should come to a consensus on a definition of uncontrolled diabetes. There are no preexisting clear-cut criteria for this designation. The following descriptions are an attempt to provide some guidance in this regard and are derived from the ICD-9-CM Professional 6th edition² and adaptation of the ADA Writing Group article on management of diabetes and hyperglycemia in the hospital.³

• A nonspecific term indicating that the treatment regimen does not maintain a patient’s blood glucose level within acceptable limits1
  
• Admission BG above 180(–200) mg/dL or two or more BG measurements during a hospital stay above 180(–200) mg/dL
  
• Lesser persistent hyperglycemia outside guidelines set by the AACE and ADA for hospital management, for example, a fasting BG above110 mg/dL and other BGs above 180 mg/dL of patients in non-critical care units, could also be considered consistent with uncontrolled diabetes.
The modifier uncontrolled only applies when the patient has a known diagnosis of diabetes.
2. Unrecognized diabetes

The key feature of the designation of unrecognized diabetes is that it is either unrecognized by the treating physician, nurse-practitioner (NP), or physician assistant (PA) or is not clearly documented as diabetes in the medical record during the hospital stay. Beyond these key features, there are no clearly defined criteria for unrecognized diabetes. It is again contingent on the hospital team, including the coding specialists, to decide on blood glucose thresholds that will be used to identify those patients who are to be designated as having unrecognized diabetes.

There is a paucity of data for guidance in defining a new diagnosis of diabetes in the hospital, and multiple variables associated with the stress of illness and hospitalization are known to affect glucose tolerance. However, it seems reasonable to accept that a patient with a random BG ≥ 200 mg/dL in the hospital has diabetes, particularly with symptoms of hyperglycemia, unless there are clearly extenuating circumstances that predispose to hyperglycemia, for example, high-dose glucocorticoid therapy. Less clear is the validity of designating a diabetes diagnosis if a patient’s fasting BG in the hospital is ≥126 mg/dL, the standard cutoff in the outpatient setting. When a patient had had a lesser degree of hyperglycemia or if the diabetes diagnosis was in question during the hospital stay, designation of a new diabetes diagnosis is contingent on the treating physician confirming the diabetes diagnosis after discharge from the hospital.

3. Diabetes complications

Accurate documentation of diabetes complications also provides opportunities for optimizing reimbursement. The ICD-9-CM1 classifies diabetes complications as:

• Renal manifestations, such as diabetic nephropathy;
  
• Ophthalmic manifestations, such as diabetic retinopathy;
  
• Neurologic manifestations, such as diabetic polyneuropathy, gastroparesis;
  
• Peripheral circulatory disorders, such as peripheral angiopathy and gangrene;
  
• Other specified manifestations, such as diabetic hypoglycemia, hypoglycemic shock, associated ulceration, diabetic bone changes, and drug-induced manifestations, for example, from adrenal cortical steroids.

Within each of these three areas of opportunity, data must be collected in order to accurately quantify the potential for improvement (baseline data) or to have a direct impact attributable to an established glycemic control team initiative (postimplementation data). Data will be gathered either by chart review and/or by extracting information from available electronic data repositories and then correlated with known financial implications of improved accuracy of documentation for the given criteria, for example, the impact on case-mix ratio and or implications for direct billing and reimbursement to the hospital.

The steps necessary to quantify each of these opportunities include:

• Defining the patient population to be assessed, such as patients with uncontrolled or unrecognized diabetes or diabetes with specific complications, as discussed above.
  
• Delineating the period to be assessed, for example, at baseline, before implementation of the intervention, or after implementation of the intervention.
  
• Obtaining DRG (or other classification system) and ICD-9 principal and secondary codes.
  
• Reviewing the implications of improved coding for the hospital’s reimbursement rates for the targeted area of opportunity; that is, if the selected opportunity (eg, uncontrolled diabetes) is correctly documented and coded, what is the dollar amount/case that could potentially be recognized by the institution based on the new DRG codes assigned to these cases.
  
• Extrapolating from the number of cases identified as having the potential to be accurately coded or from the increased number of cases accurately coded as a result of the team intervention and the dollar amount of value per case to derive a projected total dollar amount that could or has been recognized for the hospital.

EXAMPLE
Potential for improved revenue based on allowed charges for uncontrolled diabetes

Assessment of the potential for improved revenue based on allowed charges in a 344-bed MedStar community teaching hospital was conducted using the case-mix index (CMI) reimbursement system for the state of Maryland.³
Step 1. Define criteria for selection of specific population:
            Obtain APR diagnos ic-related group (DRG) and severity-of-illness (SOI) information for each case
Step 3. Have list reviewed by rates and reimbursement specialist:
            per case based on being designated as having uncontrolled diabetes
Step 4.Calculate potential for improved revenue

Item	Original (o) CMI	Improved (i) CMI
Case-mix index	0.9269	0.9750
Allowed charge/case	$8531	$8973
x 246 cases (total allowed charge)	$2,098,522 (o)	$2,207,431 (i)
Q3 potential for improved revenue (i – o)		$108,910
Annualized potential for improved revenue		$435,640

A similar process can be used to demonstrate the potential for improved revenue of systems whose reimbursement is based on a combined case rate and a percentage of charges based on contractual stipulation.

It is always advisable to use conservative, realistic assumptions when making such projections, and, again, it is crucial that the hospital finance and coding and reimbursement specialists participate in such analyses.

2. Reduction in length of stay and readmissions

Financial benefit linked to reduction in length of stay (LOS) may be assessed in one of two ways. If reimbursement is predetermined based on a DRG, shorter LOS means fewer resources are spent caring for the patient. This model is known as cost aversion. It optimizes revenue recognized per case for the hospital. Another model whereby the financial impact of reduction in length of stay can be assessed focuses on throughput for hospital beds. If LOS is shortened, there is increased availability of beds for additional billable patients to be admitted to the hospital. Newton et al. successfully applied the throughput model to obtain hospital support for a nurse–case manager diabetes management team, shown in the example below.4 Readmission within a stipulated number of days for the same DRG may have negative financial implications for the hospital. Molitch et al. have recent data (personal communication; manuscript submitted for publication) demonstrating reduction in readmission rates as a result of implementation of intensive glycemic control in patients after cardiothoracic surgery (intravenous insulin in the ICU, followed by subcutaneous insulin outside the ICU).

EXAMPLE: Reduction in length of stay and resulting increase in patient throughput

Newton et al. applied the throughput model to the results of an inpatient diabetes management program at a hospital in Greenville, North Carolina, to calculate return on investment for a multidisciplinary glycemic control team that uses nurse case managers. A 0.26-day reduction in LOS among 6876 discharged patients with diabetes was equated to 1788 days saved a year. They suggested that this led to an incremental annual inpatient volume of 350 patients, with an average LOS of 5.11 days. Multiplying this incremental inpatient volume by the hospital’s $6357 revenue margin per patient gave a throughput value of $2,224,029 for the year. They suggest that this figure would be even more significant when the expenditures averted were factored in. On the basis of salaries, consultant fees, data management, and product services expended to implement the inpatient diabetes management program, the authors suggest that throughput value allowed a 467% return on investment.⁴

The concept that intervention by a glycemic control team can have a positive impact on LOS is not new. In a 1995 article that is worth revisiting in light of the current interest in managing glycemia in the hospital, Levetan et al. showed that an endocrine and diabetes team consultation had a significant impact on LOS. The average LOS of patients cared for by a diabetes team was 3.6 ± 1.7 days, which was 56% shorter than the LOS of diabetes patients not cared for by such a team (8.2 ± 6.2 days), P < .001, and 35% shorter than that of patients who received a traditional individual endocrine consult (5.5 ± 3.4 days), P < .05. It is of note that LOS correlated significantly (P < .0001) with time from admission to consultation and that each 1-day delay in consultation resulted in a 1-day increase in LOS.7 Although the currently feasible magnitude of reduction in LOS through implementation of glycemic control teams will be less than was possible a decade ago, the concept of early intervention by a diabetes management team and endocrinologist in order to maximize the impact of the intervention on LOS — and indeed on other outcomes — is likely to be applicable today.

3. Resource utilization

Opportunities for cost savings through improved glycemic management may be assessed by analysis of geometric mean cost, expected cost for the selected practice, and comparative cost deviation between patients with and without hyperglycemia. In addition, analysis of the impact of glycemic control on morbidity and mortality will enable cost savings attributable to the inpatient glycemic control initiative to be demonstrated.

Relative to the impact of glycemic control on morbidity, mortality, and cost savings, Furnary et al. demonstrated the impact of targeted blood glucose control in diabetes patients undergoing open heart surgery (N = 4864) in an ongoing prospective, nonrandomized interventional study. Continuous intravenous insulin infusion therapy (IIT) targeting those with a BG < 150 mg/dL was found to be independently associated with reduction in risk of death and of deep sternal wound infection by 57% and 66%, respectively (P < .0001 for both). CABG surgery–related mortality (2.5%) and deep sternal wound infection rates (0.8%) were normalized to that of the population without diabetes through implementation of targeted BG control using IIT for 3 days following cardiac surgery. Taking into account both the direct and indirect costs of insulin therapy and the additional costs and LOS attributable to deep sternal wound infections, this group conservatively estimates that intensive BG control realized an overall cost savings of $680 per patient. Most of the savings was attributed to the decreased costs of treating wound infections and to a shorter length of stay in the hospital.⁷

Molitch et al. recently reported a reduction in the rates of surgical morbidity and mortality in diabetic patients undergoing cardiac surgery using IIT in the ICU followed by subcutaneous insulin outside the ICU. The authors hypothesized that the combination of IV and SQ insulin might be less costly and less nursing intensive than the 3 days of IV insulin therapy recommended by Furnary.⁸

EXAMPLE: Opportunity for cost savings through improved glycemic management

An exploratory cost analysis of data from a sample of 33% of those discharged from a 344-bed community teaching hospital in the MedStar Health System for FY 2006, quarter 3 was performed in order to identify potential resource utilization opportunities. Data were obtained from Clinical Outcomes Management and Process Analysis System, a database and software managed and licensed by Quovadx’s CareScience division. The database warehouses patient characteristics, resource utilization, and most laboratory data for all inpatients. The costs for patients with 2 or more BG > 180 mg/dL at some point during a hospital stay were compared to those of patients without hyperglycemia while hospitalized,3 as shown in Table 2. These data suggest a financial opportunity, as shown by the change in comparative cost deviation.

Table 2. Opportunity for Savings from Comparing Costs for Inpatients with Hyperglycemia with Those for Inpatients without Hyperglycemia

Outcome	Patients with ≥ 2 BGs > 180 mg/dL	Patients with controlled BG
Cases	465	1228
Geometric mean cost	$10,312	$5272
Expected cost (select practice)	$9639	$5595
Comparative cost deviation	$ 673	($ 323)
Comparative cost sig level	90% sig	90% sig

Such analyses can serve as the basis for discussion with finance and operations to obtain an estimate of potential value of the glycemic control team to the hospital.

B. Revenue generation through billings for clinical services
Implementation of targeted BG control in the hospital is an opportunity for increased pro­vi­sion of clinical consultative services for diabetes management. Endocrinologists and other physicians and allied health care providers with expertise in hospital management of diabetes, including hospitalists, nurse-practitioners (NPs), and physician assistants (PAs), can bill for their diabetes management services in the hospital. In this model, revenues generated through billings offset salary, fringe benefits, and other expenses for MDs, NPs, and PAs.

1. Nurse-practitioner support model
Northwestern University has successfully implemented a glucose management service (GMS) with the use of easy-to-follow insulin protocols guided by a formal management service. This model, implemented on inpatient surgical services using advanced nurse-practitioners in conjunction with supervision by a board-certified endocrinologist, has proven to be effective and financially viable. The NPs provide diabetes management consults and follow-up visits. Revenue generated by the consultation activity of the glucose management service NP and endocrinologist has been able to pay the salaries of two full-time NPs and an administrator and 25% of a supervising physician’s salary.^5,9

EXAMPLE: Justification of NP support through offset by billing for consultative diabetes management services

Analysis revealed that NPs on the Northwestern glucose management service did consults with be­tween 35 and 45 new patients plus follow-up consults each month in the first 7 months of 2006. Total month­ly billings for each NP averaged $13,000 for new patient consults and $12,600 for follow-up con­sults. On average, this annualizes to billings of $310,000 for each NP. If each NP is assumed to have an annual salary of $80,000 plus 30% more ($24,000) for fringe benefits, the total salary expenses to sup­port each NP is $104,000.5 Additional operating costs and contractual allowances must also be off­set in the return-on-investment equation, as illustrated in the physician support model example that fol­lows.

2. Physician support model

The case for return on investment for billings for consultative services by an attending physician, for example, an endocrinologist in the service of the hospital glycemic control team, may be made in a fashion similar to that for the nurse-practitioner and is illustrated in the following example of an analysis of return on investment that was used to obtain allocation of support for a full-time endocrinologist at a MedStar Health System hospital.

EXAMPLE: Justification of endocrinologist support through offset by billing for consultative diabetes management services

Physician Support Model for Business Case
Item	Amount ($)	Item	Amount ($)
A. OPERATING REVENUE      — Gross patient service revenue           Professional fees      — Deductions from revenue           Contractual allowances      — Net Patient Service Revenue	328,320<1     (123,504)       204,8162	B.  OPERATING EXPENSES      — Personnel (salary)      — Benefits      — Purchased services      — Risk Management      — Other operating expenses	(150,000)3       (15,000)2       (18,443)4       (11,000)2          (5000)5
Total operating revenue	204,816	Total operating expenses	(199,433)2
		C.  EARNINGS FROM OPERATIONS Net earnings	5383

¹Based on 4–5 new level 4 consults/day generating $24,000/month and 2 level 2 follow-up consults/day generating $5760/month; balance in level 3 outpatient visits; ²62%; ³1.0 FTE endocrinologist; ⁴9% billing fees; ⁵Pager/phone/printed materials/CME

It should also be noted that reductions in length of stay attributed to the diabetes case management provided by an endocrinologist or NP/PA can potentially be factored into the resulting financial benefit equation, as discussed in the length-of-stay section, when return on investment is assessed.

Other: Diabetes Education in the Inpatient Setting

Finally, financial justification for direct support of inpatient diabetes education services is currently challenging, as there is no mechanism by which inpatient education services could be billed. Therefore, the case for diabetes education is supported by incorporating the role of the educator into the business plan for the diabetes case management team as a whole. Financial support is then justified indirectly via one or more mechanisms. Net positive collections for clinical services by the team physicians and/or allied health care providers who are NPs or PAs may be applied to defray the cost of the educator’s salary. Reduction in length of stay and/or costs resulting from the team initiative may also be used in support of diabetes educator positions. The diabetes educator may also be incorporated as a member of the hospital education program in order to help meet the requirement that basic diabetes education be provided to enable safe discharge of the patient from the hospital into the primary care setting.

Conclusions

Financial justification for support of a hospital-based glycemic control team is challenging but possible, as has been shown recently by Newton and Molitch. Various models may be used individually or in combination to make the case to a hospital administration that there should be salary support for team members. Models that may be helpful in this regard include improved documentation opportunities, reduction in length of stay, reimbursement for direct clinical diabetes case management consultative services by physicians and NPs or PAs, and demonstration of improved resource utilization for the hyperglycemic patient managed by a hospital glycemic control team.

References
^1. American Hospital Directory. Medicare prospective payment system. Available at: http://www.ahd.com/pps.html. Accessed December 1, 2006.
^2. ICD-9-CM Professional. 6th ed.
^3. Clement S, Braithwaite SS, Magee MF, et al. on behalf of the Diabetes in Hospitals Writing Group of the American Diabetes Association. Management of diabetes and hyperglycemia in hospitals. Diabetes Care. 2004;27:553-591.
^4. Analysis provided by MedStar Health Outcomes Department.
^5. Newton CA, Young S. Financial implications of glycemic control: results of an inpatient diabetes management program. Endocr Pract. 2006;12(S3):43–48.
^6. Levetan CS, Salas JR, Wilets IF, Zumoff B. Impact of endocrine and diabetes team consultation on hospital length of stay for patients with diabetes. AJM. 1995;99:22–28.
^7. Furnary AP, Wu Y, Bookin S. Effect of hyperglycemia and continuous intravenous insulin infusions on outcomes of cardiac surgical procedures: the Portland Diabetes Project. Endocr Pract. 2004;10:21–33.
^8. Schmeltz LR, DeSantis AJ, Thiyagarajan V, et al. Reduction of surgical mortality and morbidity in diabetic patients undergoing cardiac surgery with a combined intravenous and subcutaneous insulin glucose management strategy. Diabetes. 2006; Abstract for ADA Annual Meeting.
^9. DeSantis AJ, Schmeltz LR, Schmidt K, et al. Inpatient management of hyperglycemia: the Northwestern experience. Endocr Pract. 2006;12:491–505.