MultiCare Health System (MultiCare) recognized it had an opportunity to improve its charge capture processes and reduce losses. The organization sought a solution that was scalable, reduced errors, and increased profitability. MultiCare leveraged its analytics platform and a charge integrity analytics solution to enhance its ability to efficiently manage its charge capture processes, providing access to timely and actionable insights and enabling efficient root cause resolution.
Clinical Documentation Improvement
Smartsourcing Clinical Data Abstraction Improves Quality, Reduces Costs, and Optimizes Team Member Engagement
Health Catalyst smartsourced two of its clients’ clinical chart abstraction efforts to significantly reduce costs, improve value, and optimize team member engagement. Banner Health and Community Health Network (CHNw) were collectively spending more than $10 million annually to manually abstract clinical measures for submission to CMS, NSQIP, tumor registries, and more than a dozen cardiovascular registries. The smartsourced relationship, leveraging the Health Catalyst® Data Operating System™ platform and a robust suite of analytics applications and improvement services has reduced costs, enhanced data quality, and improved the team member experience for these organizations.
For each heart failure admission, registered nurses at Guy’s and St Thomas’ NHS Foundation Trust collected data from five different sources, and then filled out a 10-page form for each patient. Information from the forms was then manually entered into the National Institute for Cardiovascular Outcomes Research (NICOR) web portal. This manual process for data collection and reporting was not only time-consuming and resource-intensive—but was also highly susceptible to error. To address these challenges, the organization leveraged the Health Catalyst® Data Operating System (DOS™) to integrate the data from the five source systems and extract data for nearly all of the elements required for heart failure readmissions—streamlining the NICOR submission process and improving data quality and accuracy.
Responsible for coding approximately 380,000 episodes annually, clinical coders at Guy’s and St Thomas’ NHS Foundation Trust review documentation across several systems. The overwhelming amount of data, burdensome manual review processes, and limited coding resources made reviewing all data unfeasible. To address its coding challenges, Guy’s and St Thomas’ leveraged its data platform to combine and standardise data across disparate source systems. The organization now has access to data and technology that can be used to augment coders’ work, automating data gathering to better identify patients whose diagnostic coding could be improved.
Organizations that participate in various Centers for Medicare and Medicaid Services sponsored programs must use a certified health IT product to electronically submit performance data. Acuitas Health partnered with Health Catalyst to develop a solution that would enable the organization to meet the electronic clinical quality measures (eCQMs) reporting requirements.
Albany Med’s clinical documentation improvement specialists provide high-quality care to complex, acute-care patients; however, Albany Med was experiencing lower reimbursement rates due to gaps in clinical documentation. The organization created a seamless process for clinical documentation with the use of an analytics application as driven by clinical leadership.
Improving Accuracy of Clinical Documentation Positively Impacts Risk Adjustment Factor and HCC Coding
Improving accuracy of clinical documentation can impact risk adjustment factor and HCC coding, significantly enhancing reimbursements for health systems. Read how Allina Health leveraged its analytics platform and applications to help improve HCC coding efforts and more accurately reflect patient complexity.
Financial challenges rank as the number one issue hospitals face. As a result, these organizations are constantly looking for strategies to improve outcomes, manage costs, and boost revenue. Learn how Thibodaux Regional Medical Center sustained and improved its discharged not final billed (DNFB) efforts.
Allina Health needed to ensure the data it reported to regulatory agencies was timely and accurate. The integrated health system sees 100,000 inpatient hospital admissions annually, 340,000 emergency care visits, and 6,000 physicians and 1,600 nurses providing and documenting care. Due to the sheer volume of patients and employees, clinical data abstraction at Allina Health is not a small undertaking.
Looking to stay compliant while reducing resource utilization, Allina Health sought to change its workflow procedures for faster, more accurate clinical data abstraction. A large amount of clinical data required for compliance with CMS performance measures and Joint Commission Core Measure resides in unstructured data, such as narrative notes, which require manual data abstraction. With the help of data analytics, Allina Health was able to develop evidence-based standardized processes for clinical reporting and automate some clinical data abstraction.
76 percent relative improvement in time to data availability at each site. Data is typically available within 14 days of discharge, far exceeding the 30-day target.
95.5 percent accuracy for CMS validation.
Data-Driven Clinical Documentation Improvement Program Increases Revenue and Improves Accuracy of Risk Adjusted Quality Metrics
Allina Health, an integrated delivery system throughout Minnesota and western Wisconsin, has long understood the value of clinical documentation improvement (CDI), and its growing importance in recent years. With the implementation of ICD-10, the specificity needed for accurate coding has increased, and reimbursement shifts have occurred as well, creating sizeable payment disparity for some clinical conditions. Leaders at Allina wanted to understand where their CDI program would have the greatest return on investment. However, data from the EHR was not sufficient to inform their strategy. CDI specialists still lacked the ability to perform a comprehensive assessment of the accuracy of clinical documentation, and were unable to confidently target improvement efforts in areas that would generate the greatest return on investment. To take a more data-driven approach, team members leveraged the Health Catalyst Analytics Platform, including their Late-Binding™ Data Warehouse and broad suite of analytics applications to develop a CDI analytics application. With the application, the team identified opportunities and thoroughly vetted them, before collaborating with physicians and service line leaders to educate providers on documentation improvements.
They achieved the following results:
12.1 percent improvement in CV surgical cardiology CC/MCC capture rate.
6.3 percent increase in medical cardiology CC/MCC capture rate.
Increased accuracy in publically reported risk adjusted quality metrics
Revenue capture improvement across the system – resulting in millions of dollars of additional reimbursements.
A hospital’s core mission is to provide the best care possible. To continue to do so, however, hospitals must be paid promptly. Discharged not final billed (DNFB) cases—where bills remain incomplete due to coding or documentation gaps—represent an ongoing challenge for hospitals around the country.
Thibodaux Regional Medical Center, like other hospitals, faces a myriad of new government regulations that have made hospital bill collection efforts more onerous. Its leaders recognized their inadequate manual DNFB process left hospital staff overburdened and put at risk the necessary cash flow to best serve patients.
The hospital automated and streamlined this process to relieve the burden on physicians, provide an integrated view of data, optimize visibility and workflow, and reduce the need to “downcode” reimbursements due to missing documentation. The hospital leveraged analytics to provide actionable feedback to continuously improve the process.
Thibodaux has already achieved significant improvements to cash flow and operational efficiency:
44.4 percent improvement in delinquency rate
8.2 days reduction in A/R days
70.5 percent decrease in the number of billhold accounts outstanding
50 percent decrease in physician portion of DNFB dollars
97 percent improvement in operational efficiency
How to Avoid PQRS Penalties and Earn Potential Incentives with Accurate Submission of Quality Measures
CMS has recently transitioned its Physician Quality Reporting System (PQRS) program from a pay-for-reporting program to a program that will now apply a negative payment adjustment to providers who do not satisfactorily report data on quality measures. Memorial Hospital faced a significant problem when its PQRS reporting process was hampered by its transition to a new EHR system. They needed a solution. Learn how Memorial successfully used their enterprise data warehouse to submit the necessary data to a certified registry, avoiding a four percent Medicare reimbursement adjustment, and providing them with the potential to earn an incentive payment. They also now have several patient registries that can be used for quality improvement initiatives in clinical care, patient safety, and care coordination.
Addressing The Joint Commission (TJC) core measures is a challenge for healthcare organizations. Hospital EMR data issues are well known by quality and patient safety, and clinical quality improvement clinicians. Read how this healthcare organization is using their healthcare enterprise data warehouse and clinical analytics to establish accurate baseline measures and ongoing near real time performance tracking for their TJC perinatal core measures.
The demand on hospital coders continues to rise – and even more so with the ICD-10 rollout. At the same time, health systems want to make sure professional billing charge captures are accurate. Learn how North Memorial Health System leveraged their hospital enterprise data warehouse – and the Health Catalyst Professional Billing Module – to: a) increase the number of provider notes with sufficient clinical data for billing, b) increase their monthly net income and c) improve their hospital coding staff productivity by 25%.