Enabling Informed Surgical Choices for Breast Cancer Through Shared Decision Making

One out of every eight women in the U.S. will develop breast cancer in her lifetime, and men have a lifetime risk of one in 1,000. This year, over 3.1 million women are currently being treated or have finished treatment for breast cancer.

The Virginia Piper Cancer Institute had clear evidence-based practice guidelines that directed recommendations for early breast cancer treatment options. Even with these evidence-based recommendations, however, the organization’s mastectomy rates were higher than expected.

Recognizing the organization could do better, the breast cancer program committee endorsed the spread of shared decision making for patients with early-stage breast cancer to all Virginia Piper Cancer Institute locations. The spread of shared decision making allowed patients to receive evidence-based information early in their course of care and make informed decisions that aligned with their values and preferences.

Within nine months of implementing a standard process for shared decision-making visits, the Virginia Piper Cancer Institute clinics that have completely adopted the process have made significant progress in engaging patients with early breast cancer in the shared decision-making process:

• 81 percent of eligible patients (207 people) participated in shared decision-making visits.
• 62 percent of the shared decision-making visits were in person.
• 27 percent relative increase in surgical decision of lumpectomy over mastectomy.

Care Transitions Improvements Reduces 30-Day All-Cause Readmissions Saving Nearly $2 Million

Researchers estimate that in just one year, $25 to $45 billion is spent on avoidable complications and unnecessary hospital readmissions, the result of inadequate care coordination and insufficient management of care transitions.

While increasing its efforts to reduce its hospital readmission rate, the University of Texas Medical Branch (UTMB) discovered that it lacked standard discharge processes to address transitions of care, leading to a higher than desired 30-day readmission rate. To address this problem, UTMB implemented several care coordination programs, and leveraged its analytics platform and analytics applications to improve the accuracy and timeliness of data for informing decision making and monitoring performance.

This combination of approaches proved successful, resulting in:

• 14.5 percent relative reduction in 30-day all-cause readmission rate.
• $1.9 million in cost avoidance, the result of a reduction in 30-day all-cause readmission rate.

Shared Decision-Making Leads to Better Decisions and Improves Patient Relationships

Shared decision-making is the process by which clinicians and patients work together to make decisions and select tests, treatments, and care plans based on clinical evidence. Shared decision-making balances risk and expected outcomes with patient preferences and values, empowering patients to make informed decisions.

Project leadership at Allina Health didn’t have a way to know if shared decision-making interventions were being applied. By utilizing its analytics platform, Allina Health was able to track whether or not decision support tools were being used consistently and if shared decision-making conversations were happening, if there was variation in how and when they were being used, and if they were making a difference.

Within nine months of implementing the standard shared decision-making process Allina Health substantially increased the number of patients participating in the program:

• 749 patients have participated in a shared decision-making visit across the system, including:
  • 69 percent of eligible patients with low back pain.
  • 84 percent of eligible patients with early breast cancer.

Data-Driven Practice Intelligence Increases Provider Engagement and Strengthens Improvement Efforts

Physicians are under increasing cost pressure from commercial health insurers and government payers. Physician groups that wish to remain independent must embrace the changes associated with the shift to value-based care, adopt new technologies to reduce and streamline costs, and demonstrate ongoing quality improvement.

Acuitas Health is a population health services organization that empowers physicians to make a successful transition to a value-based care delivery system. While the organization has the requisite expertise to provide these services to providers of care, Acuitas Health lacked the timely, actionable data required to effectively engage providers in improvement efforts.

Acuitas Health implemented the Health Catalyst^® Data Operating System (DOS™) to support the development of practice intelligence profiles—comprehensive views of partner practices used by the practice intelligence team to increase provider engagement and strengthen improvement efforts.

As a result of the DOS implementation, Acuitas Health improved overall data quality to achieve significant results:

• Substantial increase in provider engagement.
• 90 percent improvement in using data to identify improvement opportunities.
• Provider- and practice-specific data, which would have previously taken months to compile, is now available daily.

Improving Accuracy of Clinical Documentation Positively Impacts Risk Adjustment Factor and HCC Coding

The Hierarchical Condition Category (HCC) risk adjustment model is used by CMS to estimate predicted costs for Medicare beneficiaries, and the results directly impact the reimbursement healthcare organizations receive for patients enrolled in a Medicare Advantage plan. CMS requires that all qualifying conditions be identified each year by provider organizations. Documentation that is linked to a non-specific diagnosis, as well as incomplete documentation, negatively affects reimbursement.

Allina Health, a not-for-profit integrated healthcare delivery system serving Minnesota and western Wisconsin, needed to improve its HCC coding and clinical documentation in order to ensure the correct risk adjustment factor (RAF) was applied to its patients, since failing to do so would jeopardize its reimbursement and result in lower than expected compensation. After identifying opportunities for improvement by comparing its HCC risk adjustment coding data to other organizations and vendor metrics, Allina Health improved clinical documentation precision, medical diagnoses accuracy, and ensured eligible patients are seen each calendar year.

Results:

• 10 percent increase in RAF for the target population in one year.
• 72 percent relative improvement in four key problem list diagnoses.

Dedication to Quality Improvement Delivers on the Triple Aim: Saves Tens of Millions Annually

Unwarranted variation in clinical care is costly: representing as much as $30 million of actionable savings opportunity for a typical organization. Addressing clinical care at Allina Health, however, was challenging—as a large system with limited resources, the organization struggled to standardize work to impact outcomes and reduce costs.

Allina Health’s executive team understood that, due to market and system demands, it needed sharper focus on increasing clinical value to improve financial margins. In response, the organization launched its Clinical Value Program, a systemwide effort to measure and improve clinical value. The program quantifies the value of clinical change work to improve outcomes, while reducing costs and increasing revenue for reinvestment in care.

With a data-driven, multidisciplinary team effort, Allina Health’s Clinical Value Program has improved care and delivered on the Triple Aim, achieving the following results:

• More than $33 million positive margin impact by expense reduction and additional hospital in/outpatient revenue.
• Identified $13 million in additional opportunities for cost reductions, which have been integrated into the health system budget plan.

Collaborative, Data-Driven Approach Reduces Sepsis Mortality by 54 Percent

In the U.S., sepsis impacts more than 1.5 million people annually, of which about 250,000 will die. This accounts for one-third to one-half of all deaths for hospitalized patients. Health Quest focused on identifying ways to improve these outcomes. Despite instituting several evidence-based recommendations, the organization had not succeeded in reducing sepsis mortality to its desired rate.

Health Quest established a multidisciplinary sepsis committee to lead improvement efforts, including the use of analytics to combat sepsis mortality rates and improve patient outcomes, resulting in a:

• 54 percent relative reduction in sepsis mortality, saving 92 lives in 10 months.
• 57.1 percent relative reduction in catheter-associated urinary tract infection (CAUTI) standardized infection ratio (SIR).
• 30.7 percent relative reduction in C. difficile SIR.

Activity-Based Costing and Clinical Service Lines Team up to Improve Financial and Clinical Outcomes

Healthcare costs continue to increase at a disproportionate rate relative to gross domestic product, and Americans are becoming increasingly aware that they aren’t getting their money’s worth. To build a sustainable healthcare system, healthcare organizations must identify and address waste and reduce the total cost of care.

UPMC recognized that the common denominator to addressing threats to sustainability is to fully understand and effectively manage costs. It implemented activity-based costing (ABC), facilitated by the Health Catalyst CORUS™ Suite, to deliver detailed and actionable cost data across the analytics environment, and support service line reporting, contract modeling, and clinical process improvement. UPMC has used this approach to effectively drive cost savings and improve clinical outcomes in many of its service lines, including Surgical Services, Women’s Health, Orthopedics, and Cardiovascular. For example:

• $3M cost savings/avoidance over 2 years through the implementation of the ERAS program.
• Increased insight into cost variation and drivers of inefficiency in the operating room setting.
• Improved patient outcomes and quality (readmissions, complications, patient reported outcomes, patient satisfaction, etc.) for patients undergoing joint replacement.

Application of Analytics to DNFB Improvement Effort Continues to Deliver Impressive Results

Financial challenges rank as the number one issue hospitals face, and hospital CEOs are always looking for opportunities to boost revenue through improved reimbursement. Managing discharged not final billed (DNFB) cases, where bills remain incomplete due to coding or documentation gaps, is one important way hospitals can improve financial performance. However, without analytics to support efforts, meeting a target for DNFB improvement remains a serious challenge.

Thibodaux Regional Medical Center, a 180-bed community hospital in Louisiana, invested in analytics and resources to improve their DNFB rates. By expanding the use of analytics to every aspect of the work, the hospital transformed financial improvement efforts with impressive results.

While some organizations struggle to sustain hard-won financial improvements, two years after Thibodaux Regional launched its initial DNFB improvement effort, it has sustained the initial outcomes, and further reduced AR days by 27.5 percent, while achieving these additional improvements:

• $1 million in additional annual reimbursement, attributable to improvements in the accuracy of clinical documentation and CMI.
• 66.7 percent relative reduction in DNFB dollars, significantly improving cash flow.

Accuracy of Readmission Risk Assessment Improved by Machine Learning

Hospital readmissions carry significant financial costs and are associated with negative patient outcomes. While the reasons behind patient readmissions are multi-factorial, and the specific rates vary by institution, nearly 20 percent of all Medicare discharges nationwide led to a readmission within 30 days. Preventing even 10 percent of these readmissions could save Medicare $1 billion.

North Carolina’s only not-for-profit, independent community healthcare system, Mission Health, is comprised of seven hospitals, 750 employed/aligned providers, and one of the largest Medicare Shared Savings ACOs in the nation. Mission had been using the LACE index to predict risk for readmission, and while it was helpful, Mission’s patient population was different than the population used to develop the LACE index, leaving the health system with some uncertainty regarding the readmission risk of its patients. With the help of data analytics, Mission developed its own predictive model for assessing readmission risk, aimed at preventing readmissions and improving outcomes for patients.

Results:

• The area under the curve (AUC) for Mission’s readmission risk predictor is 0.784, outperforming LACE, and meeting the organization’s goal for performance.
• Mission’s readmission rate is 1.2 percentage points lower than its top hospital peers.