Succeeding with bundle payments: A three step strategy to start the transition from volume to value

Creating efficiencies in medical spending and more closely tying payment to quality and outcome are top priorities of the Affordable Care Act. To this end, alternative payment models are being tested by Medicare – and in some cases, mandated.

On April 1, 2016, Medicare required 794 hospitals in 67 markets to move to bundled payments for hip and knee-joint replacement surgery, in a proactive effort to reduce total episode spending and better align payments to providers with quality and outcome. This change is considered to be the first of many mandatory bundled payment programs and places participating hospitals at full risk for Medicare spending for these episodes. This applies not only to hospital services, but also for Medicare spending on physicians and post-acute care providers (PACs) during a 90-day post-discharge period. As a result, hospital systems must be equipped with the necessary tools to aggregate and analyze claims information for the entire episode of care in order to identify and reduce unnecessary spending, avoid repayment liability and, hopefully, earn gain-sharing payments from Medicare. To best prepare for the bundled payment transition, analytics platforms can help hospitals better understand, streamline and predict spending in three distinct ways: 1) analyzing Medicare claims data; 2) assessing internal hospital operating costs; and 3) applying predictive analytics for timely identification of higher-cost and higher-risk patients.

1. Extract Maximum Value from Medicare Claims Data
Under the bundled payment mandate, hospitals will receive detailed, raw Medicare claims data that report a distribution of Medicare's spending by provider for each qualifying complete joint replacement (CJR) episode, including hospital inpatient and outpatient services; payments to surgeons, anesthesiologists and other consulting physicians; and any payments made to PACs or for readmissions during a 90-day post-discharge period. In Year 1, the target episode rates will be based on three years of historical data for both the hospital's qualifying episodes and those episodes throughout its US Census Region to create a blended rate that will transition by Year 4 to be 100% regional. Each calendar quarter thereafter, hospitals will receive updated claims information as well. When this enormous amount of raw data arrives, hospital systems should have processes and capabilities in place to:

• Quickly organize the raw data
• Validate the computation of their rates
• Identify historical referral and spending patterns by provider
• Determine physician tendencies
• Identify any patient demographics that indicate greater risk for higher spend, complications and readmission
• Develop a plan to reduce spending and identify higher quality, lower spend providers as possible partners to align incentives
• Track progress throughout each performance year
• Validate the retrospective settlement after each performance year

Analytics platforms can quickly assess trends in data, highlight important information and provide meaningful conclusions about past spending behaviors that allow hospital systems to identify strategies for reducing total episode spending. Hospital systems that invest in analytic capabilities to help validate Medicare target rates and final settlements, and analyze, manage and coordinate care and spending patterns, will be more prepared for current and future bundled payment program mandates. They will also be able to proactively evaluate participation in voluntary programs with Medicare and commercial payers.

2. Drill Deep into Costs
In addition to analyzing external, retrospective data collected from Medicare claims, hospitals will need to have decision support systems in place that allow them to analyze variation in clinical practices among physicians and across service lines to identify opportunities to reduce unnecessary variation and cost. In the case of Medicare joint replacement surgery, CMS reports that approximately 53% of the total 90-day episode spending is attributable to inpatient hospital surgery and physician care during the inpatient stay. Because this 53% portion of the episode spend defines the "anchor admission" to qualify it as covered under the CJR program, and because hospitals and physicians are paid a fixed fee for these services, there is little or no leeway to reduce Medicare's spend for this part of the episode. However, the hospital can mitigate the financial risk of a repayment liability and generate a source of gain-sharing for its surgeons by reducing its internal cost of delivering care during the inpatient stay.

Advanced cost accounting systems merged with patient billing and clinical quality information form a powerful platform to identify clinical practice variation, as well as the opportunity to reduce the hospital's internal cost and create a compelling source of gain-sharing for its surgeons. Although it can be challenging to change physician habits, negotiate with device distributors and influence physician choice in use of preference items (such as implants), analytics, coupled with an understanding of the pricing structure of implants, can make these actions easier by demonstrating opportunities for physician gain-share by reducing costs per case.

In addition, hospitals that have effective daily operational dashboards in place for surgical services that track on-time starts, cancellations, case duration times by case type, room utilization and turnaround time, and throughput from arrival to discharge will be able to identify efforts to redesign care to improve efficiency and reduce waste. This will also help to reduce a hospital's internal cost and improve both patient experience and physician satisfaction. It is crucial for hospitals to invest in analytic capabilities that optimize clinical informatics, cost accounting, and decision-support platforms to drive strategies that identify and reduce variation in clinical practices that lead to inefficiency and unnecessary cost.

3. Predict the Future by Learning from the Past
Developing more sophisticated analytics capabilities will be important to maximize the hospital's ability to manage and coordinate care more effectively. By developing and applying predictive analytics, hospitals can quickly identify patients who pose greater risk for complications and readmission, and have the potential for creating an adverse impact on the hospital's quality scores. Predictive analytics can make such identifications as soon as the patient is admitted and throughout the episode care process, based upon socio-demographic information and health indicators. By using predictive analytics, hospitals can best channel their care management and coordination efforts to the patients who need them the most.

It is now more important than ever for hospital systems to prepare for the increasing transition to value-based payment, including bundled payments. An integrated analytics strategy is the key to mining actionable insights from the continuous stream of data associated with medical costs across the continuum of care. Organizations that can quickly organize, analyze and learn from this data will gain a competitive advantage in the new era of alternate payment models.

John Short is a senior manager in the Advisory Health practice of Ernst & Young LLP and is based in Dallas, TX.

The views expressed herein are those of the authors and do not necessarily reflect the views of Ernst & Young LLP.

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