For a health system, developing the analytics required to manage service-line performance across the care continuum involves an evolutionary process from rudimentary and intermediate to advanced and, ultimately, innovator stages.
Setting a Course for Growth Through Optimal Service-Line Performance
A health system cannot gain the ability to manage its service lines effectively for success under value-based payment overnight; a long-term organizational commitment is needed to develop the requisite analytic capabilities in stages through an evolutionary process.
Setting a course for growth in a value-based care environment requires a provider to have planning agility, visibility into performance, and a leadership structure that is in sync with its organizational goals.
To effectively manage their growth strategies, health systems must align the way they track and manage performance with the way they deliver care and how they are paid for that care under value-based payment models. This is a complex challenge—not only because it involves taking on more of the financial risk of care delivery but also because doing so requires using an entirely new lens through which to assess the performance of the organization. It is increasingly important for providers to understand and manage the cost of care related to episodes versus encounters.
One of the key success factors under these new payment models is having a clear means for measuring and improving episodic cost and quality, with a management structure that is conducive to managing these processes for each defined service line. At minimum, therefore, the organization’s data must be organized in such a way to support an understanding of costs across the entire continuum of care on a service-line basis to the measurement and improvement of episodic cost and quality. In short, managing cost and quality data within defined service lines, addressing performance gaps with clear accountability, and planning based on service-line projections are key success factors for growth.
These objectives can best be achieved through service-line dyad management, which pairs a clinician and administrator to manage the operations of a clinical area across a health system. Such a management structure also is becoming increasingly important for improving quality and driving organizational growth. Supporting this dyad model with effective data analytics provides the necessary foundation for improvement, service-line reporting, and a strong organizational infrastructure that fosters continued improvement and success.
A primary benefit of this approach to managing clinical services is that the service-line structure is organized along the patient’s continuum of care as opposed to the organization’s department structure. Cross-departmental leadership and staff are brought together to manage similar patient populations and align better with the patient’s experience. The approach allows for a more efficient allocation of organizational resources, such as staff and capital, and enables an organization to more quickly assess vulnerable areas and adjust to market forces.
When designed and implemented appropriately, service-line management can substantially reduce both clinical and operational costs associated with managing a patient population, thereby enabling the organization to capture value-based revenue and improve margins—essential outcomes for success under value-based payment. At the same time, service-line management can promote reduced clinical variation and improved care coordination, thereby improving quality. This combination of reduced costs and improved quality positions the organization for growth through improved margins and increased market share.
Evolution of the Service Line
Although approaches differ, service-line management represents fundamentally a cultural and tactical shift that, in addition to strategic planning and investment, requires leadership buy-in across clinical, financial, and operational departments.
Effective service line management also takes time to mature. It evolves through stages defined by progressive capabilities (i.e., from beginning through intermediate and advanced and, finally, to innovator).
The foundation of the service-line management approach is service-line analytics— data that are accessed, analyzed, and reported upon for a specific patient population. Once the service lines are clearly defined, a structured analytics strategy will ensure that stakeholders are receiving consistent and accurate data to drive action. Such analytics are used to identify the cost and profitability of service lines. This is the first step into understanding areas of opportunity for growth and areas of waste and inefficiency within the care delivery system.
Here, we describe the characteristics of each stage, including key capabilities in employing analytics and the various tools that can best facilitate data analysis at each stage to provide for more timely reporting and, ultimately, to promote further service-line maturity and growth.
The most significant undertaking at the onset of a service-line strategy is to gain consensus within the organization around the plan to set up service lines. Once that consensus has been reached (either horizontally across business units or through an executive directive), executing on this plan becomes tactical. Establishing service line management starts with defining the service line’s patient population (e.g., cardiac patients) and working to improve access to data on the population as well as end-user acceptance and validation of the data. Off-the-shelf patient population definitions are available through a number of healthcare industry organizations.
Acceptance and validation of the data require obtaining the feedback and approval of clinicians to ensure that the definition of the patient population accurately reflects their definition of the patients they manage daily. For example, using billing codes to define a patient population may be too broad, whereas using a patient’s scheduled procedure may inadvertently exclude some patients. Clinicians should review the definition and provide input to confirm that everyone agrees to the measurement methodology.
Once the patient population definition is approved, the data should be considered a part of routine decision-making, tracked on an ongoing basis rather than simply viewed in a one-time report. The data should be easily updated, flexible, and reviewed frequently by operations and leadership. Weaving these data into day-to-day operations begins to provide visibility into service-line analytics and helps the organization build a foundation to support additional service-line growth.
Tools. The tools used at this stage are intended to provide service-line structure and visibility, generally leveraging a decision support platform. They should encompass software, for example, that allows for defining the service line and leveraging data such as MS-DRGs, ICD-10 codes, and other patient-defining characteristics (e.g., age, payer group, and clinical test results) to assist in grouping patients into cohorts for additional analysis. The tools then must be leveraged by the financial, operations, and clinical stakeholders to define the patient populations that will then be used for analytics.
Case example. In 2016, the CEO of a three-hospital system in Texas outlined a strategic goal to transition the way the organization analyzes and manages its performance from a department model to a service-line model. Leadership tasked the finance team with defining the service lines and communicating the new approach across the organization.
To foster an organizationwide commitment to this goal, the CFO established a team of leaders from operations, marketing, strategy, and clinical areas, with the goals of educating stakeholders and determining the most appropriate service-line categories to use. The cross-functional team developed a plan to communicate the new model to stakeholders across the organization, focusing on the value and benefits of a service-line strategy. For the process of selecting and validating service-line categories, the cross-functional team addressed common categorization questions (e.g., whether transplants should be organized by MS-DRGs or ICD codes) while the finance team was charged with running the analysis of volumes and with understanding the percentage of patients that apply to the service lines within each categorization method.
Once the service lines were clearly defined, the information was disseminated to the rest of the organization, and the team held additional communication sessions to ensure all necessary groups understood the new structure. The service-line definitions were then loaded into the organization’s decision support tool, and leadership immediately began tracking volumes by service line.
In the next stage, the organization gains visibility into volumes and financial performance data. Tracking basic cost data (e.g., labor, supplies, and ancillary services), measuring contribution margin, incorporating quality measures into the analytics, and developing reports and dashboards for various stakeholder audiences are key to having intermediate proficiency.
Tools. At this stage, the decision-support platform (costing tools and reporting dashboards) should integrate data from clinical and operational areas to provide visibility into basic cost data for a service-line patient population. Decision support costing is critical for service-line maturity because it provides visibility into sources of cost variation and unfavorable financial performance. The tools also can enable analysis of data on measures of quality to identify adverse events that occur during the inpatient stay. These data should then be compared to readmission data to assist providers with prioritizing quality improvement initiatives.
For example, an analysis of patients with acute myocardial infarction and those undergoing coronary artery bypass graft can present pertinent data for an episode of care, such as total costs, direct costs, net revenue, and expected payment for all visits that occur during the 90-day episode. These data can then be used to calculate the contribution margin and operating margin for the service line and analyze trends in readmissions.
Case example. A community health system in Illinois has been leveraging its decision support system to implement financial performance-tracking at the service-line level and incorporate the quality of outcomes into its analysis. The finance team has been partnering with clinical and operational leaders to develop cost and quality dashboards. This exercise has led to a review and increased understanding of how costs are calculated and what are the inputs that drive cost.
Clinical and operations leadership now have more visibility into service-line margins and revenue. Incorporating quality data into the financial dashboards has also provided new insight into how adverse events affect length of stay, readmissions, cost, and the service lines’ bottom lines. Finance now meets regularly with operational and clinical stakeholders to review the data and begin discussing performance goals. Once the goals are solidified, a regular meeting cadence is established to review the dashboards, service-line performance, and trends and report variances from the goal.
At this stage, data analytics are actionable, which means the data can be used to identify ways to reduce costs and improve quality. Opportunities for reducing cost and improving quality are routinely identified and implemented, and results are tracked monthly, with physician and executive engagement. Achieving this stage of service-line management is important as organizations begin to shift from fee for service to fee for value. In fee-for-value payment arrangements, the only way an organization can increase its profitability is to improve the value of the care it delivers by improving quality while reducing the cost.
Organizations that have a culture of continuous improvement and see the reduction of cost and waste as an ongoing priority will be in the best position for future growth.
Tool. At this stage, with a focus on continuous improvement and project management, advanced service-line management combines the use of decision support tools with process improvement functionality to identify cost savings opportunities, prioritize the opportunities, provide drill down analytics into the data, and then track improvements over time. As a result, users can spend less time analyzing data and more time driving organizational change.
Continuous improvement tools, used at different levels at the intermediate and advanced stages, review multiple types of data, including general ledger, cost accounting, patient charges, diagnosis codes, contracted rates and payment, supply acquisition costs, payroll, time and attendance, and admission/discharge/transfer data to identify and prioritize savings opportunities. Through algorithms and workflow, the toolset should enable a process and accountability structure for improvement. The toolset assists in identifying, evaluating, prioritizing, and planning cost-saving initiatives, and in tracking progress toward goals for initiatives and for the overall organization.
A more sophisticated analysis that an organization can perform at this stage involves looking at cost accounting data in areas such as supplies, pharmacy, ancillary services, and length of stay to detect differences in care delivery patterns that lead to unnecessary variation in care and cost. Users should be able to drill down into these variations in utilization, such as supply usage by provider, and compare provider costs against the median supply cost per case. The tool should facilitate conversations by providers on how to better manage the cost of care without affecting the quality of care.
Continuous improvement tools reduce the time spent on the “heavy lifting” of analytics required to identify opportunities and track progress, thereby allowing for more time to be spent driving action to reduce costs and improve quality.
Case example. A community hospital in the Northeast has is performing analyses to identify opportunities for continuous improvement tool on top of analytics for decision support. As a result, the hospital has identified cost-reduction opportunities that total 5 percent of the hospital’s annual operating expense. The organization’s goal is to reduce its costs further over the next several years, and its finance team has begun partnering with clinical leaders to investigate ongoing opportunities for improvement, establish cost-reduction goals, and begin to track performance against these goals. One cost-reduction opportunity that has been identified is the use of a very expensive stapler in nephrectomy cases. One stapler costs $500, while a stapler that other surgeons use for the same procedure costs $200.
Once a cost-reduction opportunity is identified for a service line, it is assigned a project manager/owner within a performance-improvement group, which is charged with analyzing the data underlying the opportunity and meeting with clinical and operational leadership to establish a realistic cost-reduction goal for the fiscal year.
The performance-improvement group’s finance owner and clinical leader then meet with clinical stakeholders together to review the data, obtain feedback, and discuss how the goal will be measured for the upcoming fiscal year. Monthly progress towards the goal is then reviewed at the project level, service-line level, and leadership level. In our example, financial and operational leadership met with the urological surgeons to review the cost of the staplers and when each type is used. The urological surgeons did not realize there was a $300 cost difference between the two staplers and did not believe one was more clinically effective than the other. The group agreed to eliminate usage of the more expensive stapler over the next fiscal year. Based on historical volume and usage patterns, this change equated to cost savings of approximately $20,000 for the year. This goal was entered for this savings opportunity, and progress is now tracked monthly.
At the leadership level, the organization reviews overall progress toward the annual goal, which projects are not tracking toward their goal, the reasons behind the variance, and how can they assist with getting the project back on track. Goal alignment at the leadership level and physician engagement is critical to the success of this program. In our example, leadership support is important to help the organization understand why even small cost-reduction savings are important and worth pursuing. Leadership and clinical support also may have been necessary if the urological surgeons did not want to change staplers and could not provide clinical justification for keeping the more expensive stapler in stock. Leadership support also is important if the savings opportunity requires an up-front investment to achieve—for example, IT modifications, investment in new drugs or supplies, or funding of additional resources to achieve results.
The cost reduction goal is a year-over-year goal. Therefore, the hospital is currently preparing to review system-identified cost savings opportunities for FY19. Cost savings opportunities will be reviewed during the budget planning process to assist with the closing of budget gaps. Improvement goals will be incorporated into the budget and will assist with FY19 goal alignment. It is important to have systems that have automated tracking capabilities so that previous fiscal year improvement efforts can continue to be tracked along with new initiatives, thereby reducing the risk of backsliding on improvement gains or, at minimum, identifying that possibility so project teams can be mobilized to correct course.
At the innovator stage, with a clear line of sight into margin and quality data, as well as a structure to improve on those metrics, providers should be able to assess and define patient populations that qualify (low variance, high volume) for risk-based contracts with commercial payers.
Tool. Health systems that have become innovators in service-line management can begin to apply sophisticated analytical tools and techniques to set up and evaluate value-based programs that benefit both provider and the insurer. At this stage, they have the ability to analyze patient populations based on payer contracts and reimbursement terms. For example, by applying “episode grouper” capabilities, a health system can analyze which encounters fall within or outside the scope for bundles. The output shows service-line leaders the projected and possible financial performance of specific bundles, and allows them to identify high-volume, low-variance episodes, which could qualify for bundled terms.
Case study. In early 2016, a group of physicians at a cancer center in the Midwest who were closely engaged with their service lines’ performance and strategy, realized they were uniquely positioned in the marketplace to deliver more world-class care and improve margins. With the goal of proposing bundled services to their commercial payers, they set out to develop a tool for analyzing current volumes, quality, and cost data to ensure the bundle target metrics would be achievable within the populations that would be served under the bundled payment contracts. By using the tool to analyze episodes and identify specific patient populations for scenario modeling, they were able to determine which episodes of care would be most beneficial to their patients and their organization. The innovative tool also enabled the group to identify, track, and adjust patient populations over time, giving them a basis for negotiating with payers on innovative new payment models.
Beyond the analytics and tools, effective service-line management requires a strong organizational infrastructure that supports planning, performance management, and process improvement initiatives within service lines. This infrastructure includes the following key components.
Leadership buy-in. An essential element in any improvement initiative is buy-in and support from all areas of leadership. Success depends on the extent to which clinical, operational, and financial leaders understand the scope of the service-line priorities, share a commitment to the same goals, rally support among their various teams, and drive accountability at all levels of the organization.
A dedicated team. Tools can identify opportunities, set targets, and prioritize work—but they cannot drive change. Also essential is the work of dedicated individuals who can manage a cross-functional project team to deliver results. Project managers should be able to focus solely (75 percent of their time, at minimum) on process improvement initiatives. Managers who are juggling these tasks with other work will find it challenging to carve out time from their daily operational duties to achieve results.
A change methodology. Part of the supporting structure is a methodology for change, which may include process improvement approaches as Lean, Six Sigma, or PDSA (plan-do-study-act). Coalescing around one approach to problem-solving will provide structure and a common language for project teams. It also can help leadership understand how a project runs and make change more comfortable. Change is hard, but having a methodology and a routine for change can help weave process improvement into the organization’s culture and make change less scary.
Continuous reporting. With all performance improvement initiatives, organizations face the challenge of sustaining progress once the project has been completed. If data are not updated and revisited continuously, an organization runs the risk of backsliding and losing the gains achieved by the project team. A core benefit of a continuous improvement platform is that it automates reporting, which can be the key to sustainability. Performance data must be presented to stakeholders regularly in a concise and easily accessible manner. If their work is effective, the data will reflect that success; if not, the need for other approaches will be clear, allowing for faster course correction to sustain improvement gains. Continuous, automatic data reporting makes it easier to track progress and sustain results.
The Essentials for Growth
Service-line management driven by analytics and sophisticated tools enables healthcare leaders to better steer their businesses to success in a continuously changing environment. Meanwhile, organizations also require the essential ingredients for sustainable success: an infrastructure of strong leadership, dedicated management, and oversight, and a culture of process improvement.
Jennifer Ittner, MA, is director, continuous improvement, Strata Decision Technology, Chicago.
Alina Henderson, is director, professional services, Strata Decision Technology, Chicago.