Cost Accounting in healthcare via Decision Support has always relied on core clinical systems for revenue, quality, episodic, and statistical variables. Supply and labor cost components via allocation methods from the finance system, however, continue to challenge true service and product line costs of services.
Evolving healthcare cost accounting now depends on an underlying Data Warehouse (DW). Data Warehouses, in one form or another, have been around for some time now; however, what is new is the idea of an “Enterprise” Data Warehouse (EDW). Enterprise as defined by a DW that supports the Health System’s data needs as a whole, and not just the Decision Support (DS) application. As these EDWs are coming to market, they are increasingly being delivered with both Physical and Logical Data models (Star Schemas) and supporting Extract Transform and Load (ETL) tools to bind the health system’s clinical data with the finance system as the core data repository for “costs.” Now, for those who have not acquired a Data Model or are preparing to take the next Cost Accounting leap, here is a path for merging Episodic data with Financial “transactional data” for a properly designed financial and clinical system cost process flow.
Figure 1 – Financial and Clinical System for Episode Cost Process Flow
In Figure 1, the data flow illustrates a data process to determine episode cost of care from the financial and clinical system to begin to understand the true cost of care as parsed into product/service lines for a Healthcare Cost Accounting Model (Figure 2). Note the “leverage optimized CoA for Functional view” as the General Ledger Structure should contain a GL String to allow mapping to a Service, Product Line, and/or Physician. With an “optimized” CoA, a Healthcare Cost Accounting Model will allow a health system to leverage a multidimensional view of both cost management and quality of care data to address critical clinical outcomes and revenue cycle optimization objectives based on actual data and reliable actionable information.
Figure 2 – Healthcare Cost Accounting Model
|Healthcare EDW Model||Shows that a decline in operating margin can be attributed to increased usage of a specific implant that drives up cost without demonstrating a corresponding improvement in patient outcomes.|
|Medical Claims Data Marts||Facilitates reporting on outcomes related to specific procedures performed on various patient populations.|
|Operation Data Marts||Supports reporting on vendor spending, medical supplies, staffing, and other administrative data.|
|Financial & Other Data Marts||Supports reporting on key financial metrics such as patient revenue and direct and indirect costs.|
Traditional Health System Decision and Data Warehouse are siloed and have the following:
- Limited cost or financial key financial metrics to service and product lines
- Narrow views of clinical data to facilitate reporting on patient outcomes
- Challenges with clinical, financial, and admin data integration to tie financial metrics with patient outcomes
- Lack many high value analyses
A Healthcare Cost Accounting Model, as illustrated here, provides the following:
- Financial, clinical, and administrative data integration via a Financial and Clinical System for Episode Cost Process Flow
- Integration into a common data repository for advanced interactive analytics
- Supports Product and Service line analyses
Table 1 – Healthcare Cost Accounting Model Business Objective
|Clinical Outcomes & Patient Experience||
|Quality and Safety||
Why Integrate and Design a Financial System with the Clinical System
Hospitals are being squeezed as costs are soaring, and payer reimbursements are declining. Take Pay-for-Performance, for example: substandard care leads to higher costs (e.g., longer hospital stays and unnecessary, repeat hospital visits). Insurers are capping payments for services or denying payment unless providers prove the cost of care they provide is consistent with recognized standards for quality. Hospital stakeholders are asking hard questions, looking for ways to maintain or even improve operating margins without compromising quality of care. The bottom line is that stakeholders need accurate, timely, actionable information to comply with external reporting requirements, and to make informed strategic, tactical, clinical, and operational decision-making.
Action Next Steps
Step 1: Revisit cost accounting with the following key components. A properly designed Cost Accounting Enterprise Data Warehouse model depends on a multi-dimensional Chart of Accounts (CoA) that is designed for Service and Product lines. This alignment provides the foundation for all costs mapped to patient care.
A properly designed integrated Financial System with the Clinical System will support cost accounting, patient analytics, and performance management through budgeting, flex variance reporting, and productivity management to support actionable Business Intelligence. Figure 2 – Healthcare Cost Accounting Model provides the foundation:
- Purpose-built data warehouse model
- Finance System to Clinical System always being parallel (automatically)
- A no-contention, data-shared model
- BI simplicity via data governance
- Foundational architecture model for:
- Predictable performance
- Predictable scalability
- Mixed workload
- Any query, any time
- A mature, purpose-built, optimized cost accounting data model
- Data process flow
Step 2: Revisit the health system’s separate Data Warehouses and decide as a strategic goal to move to a single EDW. Begin building a unified EDW via a Financial and Clinical System for Episode Cost Process Flow. This Step will consist of prototyping a set of executive-level “BI dashboards” showing performance in the patient care space encompassing Clinical, Operational, Financial, and Regulatory metrics. The goal is to provide project “specifications” for the timely delivery of easily accessible, consistent cost summary data across the enterprise. Future steps should then be planned to provide more sophisticated analytics around patient care, as well as providing Business Intelligence and Analytics around Service Excellence, HR, Quality, Revenue, and Supply Chain.