Clinical business intelligence is important for health systems leaders who are transforming their organizations into efficient vehicles for the delivery of high quality care. Data analysis to improve care is being embraced and moved to the center of organizational strategies due to a confluence of several trends. This new urgency is a result of national trends in economics and politics and of the looming transition to new business models for healthcare providers. Health systems are consolidating and leveraging new information technologies to succeed in this changing environment.
Economic and Political Drivers
Pressure on Prices -Overall spending on healthcare in the US has been growing rapidly for many years and is expected to continue to grow into an increasingly large portion of the nation’s economy, taking a larger chunk out of family budgets. The overall volume of services delivered by health systems is expected to continue to rise significantly, which could be taken as a healthy sign for the business of healthcare. But with the US economy recovering slowly from a recession – and with average family incomes rising very slowly at best – governments, private payers and patients are resisting price increases in order to keep their rising consumption of healthcare services from busting their budgets.
Government Budget Cuts - The demand for medical care is expanding as the US population ages, and cuts to future Medicare reimbursement rates for hospitals and doctors are frequently on the table as the federal healthcare budget comes under pressure. While the healthcare reform legislation of 2010 focused largely on expanding access to healthcare, it also set in motion a number of initiatives intended to get costs under control.
When compared to writing-off the whole amount as charity care, the expansion of Medicaid programs to cover previously uninsured patients will improve revenues for providers. However, Medicaid rates are generally so low that hospitals still lose money on every patient. Healthcare executives know that their organizations will have to learn how to deliver care more efficiently in order to thrive under the relatively low Medicare and Medicaid rates that will prevail in future years.
Consumerism -Revenues from patients with private insurance plans are also under pressure. Many employers have been unwilling to bear the additional costs of rising insurance premiums and have shifted more of the expense to their employees in the form of higher co-pays and deductibles, as well as larger deductions from paychecks. These changes are leading patients to act more like consumers who are sensitive to price and shop for the best value. While the current impact has been limited, the effects of consumerism on healthcare pricing are expected to grow over time and to put additional pressure on healthcare prices as patients start to act more like shoppers.
Transparent Quality - At the same time as the public and their political leaders have become more concerned about the costs of healthcare, scientists and policy makers have been sounding the alarm about problems with the quality of medical care in the US. Reports issued by the Institute of Medicine (IOM) several years ago were a wake-up call for the industry and scrutiny of quality has steadily increased. However, actual progress in improving quality for the country as a whole has been very limited, despite many local success stories. The IOM’s 2013 report, US-Health-in-International-Perspective-Shorter-Lives-Poorer-Health, on the poor quality of healthcare in the US compared to other countries is enlightening.
CMS now requires hospitals and doctors to report numerous quality measures and publishes some quality scores publicly. Private insurers have their own measures of quality. Some are using quality and cost data to structure tiered networks that can steer patients to preferred providers, using the carrot of lower copayments. While the influence of objective information on patient decisions about healthcare quality has been limited, experience in other markets indicates that, in the future, consumers may be influenced by information on the relative quality of healthcare providers. With payers and patients all looking at quality metrics, healthcare leaders are seeing an urgent need to excel on those measures.
At-Risk Business Models
The healthcare reforms enacted by the federal government affect healthcare providers in many ways. One of the most fundamental impacts of the legislation is to forcefully push the industry down the road towards “at-risk” business models, shifting away from traditional fee-for-service payments towards models where the provider payment is fixed in advance. This shifting of risk to providers is a key strategy by the Centers for Medicare and Medicaid (CMS) and other payers to reduce the rate that healthcare cost increases by using providers as their allies in cost reduction.
In traditional fee-for-service medicine, doctors and hospitals are paid for each procedure, treatment or test that their patients need. They suffer no penalty for inefficiency (e.g., ordering extra services) and may be even be paid more if they make mistakes that lead to a need for additional treatment. In the fee-for-service model, the payer benefits when fewer resources are consumed -- but the provider is paid less. But when a provider is “at-risk” of losing money if the amount of care needed by patients exceeds expectations, he or she has financial incentives to provide care efficiently. However, “at risk” providers can make money by being more efficient. The degree of provider risk varies based on the scope of services covered by different payment methodologies. CMS payment methodologies, and similar initiatives by private insurers, are pushing more and more risk onto providers.
Inpatient Efficiency Risk – Hospitals have been managing a modest amount of financial risk since the introduction of DRG case payments decades ago. Under the inpatient prospective payment system, hospitals receive a fixed payment per case. This is based on the patient’s medical problem or surgical procedure, as defined by the ICD-9 diagnoses and procedures coded for the patient. While actual costs might vary from patient to patient, payments were essentially fixed. Changes to the payment formulas in recent years have increased the amount of risk born by the hospital by:
- Basing the grouping of the patient into a DRG only on diagnoses present on admission
- Not increasing payments for patients with certain complications of care (e.g., hospital-acquired infections).
Quality Risk – In recent years, CMS has phased in Value-Based Payment Modifier methodologies that add to the amount of risk that hospitals are responsible for in managing inpatient stays by Medicare patients. Earlier, hospitals were merely required to report detailed quality measures to CMS in order to receive their full DRG payments. Now a portion of the DRG payments may be withheld if the hospital does not score high enough on a suite of quality measures that address:
- Compliance with process of care standards
- Patient survey results
- Rates of readmission.
These penalties are growing from 1% to 2% and can be significant, as Medicare often accounts for nearly half of total hospital revenue and hospital margins are often in the single digits. Analyzing the factors that lead to poor quality metrics and improving on those clinical performance measures is now a financial imperative. Information on the Value-Based Purchasing program is available on the CMS website.
Episode Risk – The CMS Center for Innovation has also experimented with payment methodologies that put the provider at risk for the cost and quality of inpatient care and for related services provided before and after the inpatient stay. For procedures like joint replacement, for example, a “bundle of care” may include physician office visits, surgeon fees and rehabilitation services – in addition to the hospital services. To implement bundled payments and effectively manage the care for episodes, health systems need clinical analytics solutions that can:
- Aggregate information from all settings of care
- Align them by episode
- Track costs and quality for each episode type
The Healthcare Financial Management Association also provides useful information about bundled payments on its website.
Population Risk –Taking on responsibility for managing all the care needed by a population of covered members involves an even higher degree of risk. Private insurers take on this type of risk when they enroll members in a Medicare Advantage plan. Under these plans, the insurer receives a fixed capitation payment from Medicare and then pays hospitals, physicians and other providers for all the care the members need over the course of the year.
Regulations issued to implement the healthcare reform law have defined new payment mechanisms that encourage provider health systems to organize themselves as Accountable Care Organizations (ACOs) that can take on this type of population risk themselves. While the ACO model stops short of full capitation, it provides various formulas allowing the health system to participate in Shared Savings when its Medicare population uses less healthcare resources. This model is intended to align the provider’s financial incentives with CMS so they can both benefit financially from improvements in efficiency.
As they plan for and start to implement these ACO models, provider health systems need to use new types of clinical analytics solutions that include population health performance measures (similar to those that payers use).
Even as healthcare reform was being debated, health system leaders recognized the trend towards at-risk business models and starting transforming their organizations to succeed in this new business environment.
Many health system executives have concluded that they must own pieces of the healthcare delivery system beyond the hospital so that they can coordinate care more effectively and reap the benefits of efficiency and scale. Many health systems are acquiring other providers to create integrated delivery networks (IDNs). These IDNS can include
- Acute care hospitals
- Primary care physicians
- Specialty care physicians
- Ambulatory surgery centers
- Rehabilitation care
- Home health care
- Long term care
As health systems deliver services in these additional settings, the scope of clinical analytics must grow to integrate new performance measures so that the whole system can be managed effectively.
Information Technology Enablers
Health IT provides tools that can help health systems succeed in the process of clinical transformation. The American Recovery and Reinvestment Act (ARRA) of 2009 provides incentive payments to hospitals and physician practices that implement electronic health records (EHR) systems. To qualify for the incentive payments, providers must meet a set of ‘meaningful use’ standards set by CMS to ensure that the EHR systems and complementary technologies provide a broad set of capabilities that are expected to enable higher quality and lower costs of care. The incentive payments have spurred widespread adoption of these systems by hospitals and physicians.
The Topics and Tools section of the HIMSS website includes extensive information on Meaningful Use.
Having made these large investments in EHRs, health system executives see the use of clinical analytics to improve performance as a major opportunity to realize a return on those investments. With the rapid adoption of systems spurred by meaningful use, healthcare providers are accumulating more clinical data in their databases. They are starting to use clinical analytics to “mine” that data for insights and intelligence to improve the care process. For many purposes these new sources of clinical data are more timely and specific than the billing and claims data that have been used in the past.
As they start to use clinical data for quality reporting and process improvement, health systems are recognizing the importance of capturing quality measures (CQMs) in a discrete, structured format rather than as free text. To fully realize the potential of clinical analytics to support clinical transformation, EHR systems must address discrete data capture and map the flow of clinical data elements into systems for reporting and analysis. One of the many organizations working on this issue is the HL7’s Quality Information Work Group, formed in January 2013.