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.
Quality & Process Improvements
At MultiCare Health System (MultiCare), inconsistent application of improvement methods, differing competencies, misaligned projects, and inefficient performance data collection were impeding the organization’s ability to improve, leaving quality and operational metrics below expectations. Using a data platform and a robust suite of analytics applications, MultiCare has integrated analytics support into its improvement teams, reducing hospital length of stay (LOS), and achieving significant cost savings.
Banner Health identified considerable variation in surgical supply use across its facilities. The health system desired a collaborative, data-driven strategy that would allow it to maintain high-quality outcomes while simultaneously decreasing costs across all procedures systemwide. To standardize supply use, Banner Health implemented an analytics application to help identify high-volume, high-cost surgical procedures that varied across the system. It then built standardized surgical preference cards for the high-volume procedures.
Thibodaux Regional Health System recognized its patients with Type 2 diabetes had hemoglobin A1C (HbA1c) levels that exceeded the evidence-based guidelines for blood glucose control and sought to improve the health of this patient population. Using a data platform and a consistent improvement methodology, Thibodaux Regional learned more about the challenges to diabetes self-management in its population. The organization was then able to improve its outreach and support for patients with diabetes.
Thibodaux Regional Health System sought to transform its blood transfusion practices. Aware that blood transfusions were often over-used, Thibodaux Regional’s leadership prioritized engaging providers to decrease unnecessary transfusions, reduce potential risks to patients, and improve costs of care. The organization leveraged data and analytics to better understand opportunities for improvement.
Memorial Hospital at Gulfport (Memorial) knew that decreasing clinic no-show rates was an opportunity to increase revenue, eliminate delays in care, and improve care coordination for its patients. With a robust data platform, Memorial leveraged its data and analytics to better understand the reasons behind its high no-show rates. With actionable data, the organization implemented measures to effectively improve its no-show rates and increase revenue.
Caregiver satisfaction and voluntary turnover at Community Health Network (CHNw) were negatively impacted by a lack of standard processes, which resulted in rework and communication gaps regarding procedures and increased labor costs, reducing CHNw’s environmental services (EVS) team’s ability to deliver core services. In an effort to reduce voluntary turnover rates, CHNw convened an improvement team to improve processes, training, and communication related to cleaning requests, which has led to reduced voluntary turnover and labor costs.
Community Health Network identified that inconsistent oversight of durable medical equipment (DME), and process variation, were a likely source of waste and lost revenue. The health network sought a systemwide, data-driven process for the purchasing, dispensing, and billing of DME. A data platform and analytics applications were utilized to understand organizational performance, identify opportunities for improvement, and evaluate the impact of these changes on patient, financial, and organizational outcomes.
Community Health Network, a hospital system in Indiana, discovered that its hospital-acquired C. diff infection (HA-CDI) rate was higher than the national benchmark. The organization knew it needed to decrease infection rates, but without timely, meaningful data, leaders couldn’t identify the right areas to focus improvement efforts. With the use of a high-level, robust analytics system that allowed better access to data, team members were able to determine where to focus their efforts.
This healthcare organization, comprised of a specialty hospital and multiple clinics, sought to improve safety for its patients, focusing on identifying wrong-patient order errors. To better understand and improve patient safety, the organization needed to move beyond passive surveillance. By using multiple detection methods for identifying wrong-patient errors and establishing triggers that identify when a wrong-patient order may have occurred, hospital and clinic staff are able to investigate instances.
Community Health Network (CHNw) observed higher than national rates of maternal substance use disorder, with a higher number of pregnant women having positive drug screens for opioids, cocaine, amphetamines, barbiturates, and benzodiazepines. It developed a care coordination and substance use program to help reduce the incidence of substance use disorders among pregnant women. Using its data platform and analytics applications, CHNw was able to evaluate the impact of various process measures on patient outcomes.
Thibodaux Regional Health System had improved sepsis mortality rates, yet it recognized the challenges associated with sustaining and further improving care outcomes. Driven by a mission of patient-centered excellence that starts with the chief executive officer, the Board, and leadership, Thibodaux Regional’s sepsis care transformation team utilized its data platform and analytics applications to help facilitate data-driven, sustained improvements.
UnityPoint Health created a task force to develop and implement a plan for maximizing blood management. The task force incorporated decision support to improve transfusion ordering in alignment with the transfusion standards. An analytics platform has also been leveraged, which monitors the utilization of blood products, including predictive modeling to risk-adjust blood utilization specific to patient case-mix, and data down to the ordering provider level.
Length of stay (LOS) is an essential indicator of hospital operational efficiency. Albany Med compared its performance with benchmark data and determined that it could improve inpatient LOS. By convening a multidisciplinary team of providers committed to decreasing hospital LOS and leveraging its data and analytics platform, Albany Med was able to uncover underlying issues causing unnecessary extended hospital stays and substantially reduce LOS.
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.
To improve the care of patients with Clostridioides difficile (C. diff), Thibodaux Regional Medical Center used data and analytics to monitor completion of early recognition screening, protocol compliance, antimicrobial stewardship rounds, and probiotic administration. Thibodaux Regional’s leadership, culture, evidence-based practices, and data-driven improvement approach have positively impacted its patients.
Managing and retaining a talented workforce represents approximately 60 percent of hospital costs. In an effort to improve staffing efficiency, Hawai‘i Pacific Health (HPH) sought to realign its staffing practices to better manage and predict its labor needs. Utilizing its data platform and analytics, HPH was able to forecast its workforce needs and effectively manage staff schedules—two changes that led to significant cost savings.
Mission Health trauma services provide evidence-based care. Despite its efforts to measure the impact of this care on outcomes, the overwhelming burden of manual data review limited its ability to effectively monitor key process measures in a timely manner. This prompted Mission to use data and analytics for timely insights into injury-specific process measure performance and concurrent chart review to improve trauma care.
Actionable Analytics Enables Improved Care, Reduced LOS, and Costs in Patients with Traumatic Brain Injury
To provide high-quality, cost-effective care to patients with traumatic brain injury (TBI), Mission Health needed insight into individual patient and provider performance data. Without access to accurate data, Mission couldn’t accurately pinpoint patient outliers, understand causes of TBI, and identify opportunities to improve TBI patient care. By utilizing its data platform and analytics accelerators, Mission was able to utilize patient data to identify patients suffering from TBI.
To leverage data from its trauma registry database to improve patient outcomes, Mission Health utilized its data platform and analytics, providing real-time access to data and a more comprehensive understanding of each trauma case. With an analytics-driven approach, Mission reduced emergency department (ED) length of stay (LOS) for patients with Level II trauma activation across its populations.
Annually, U.S. hospital supply chain overspend costs an estimated $25.4 billion, which represents 30 percent of all hospital spending. Utilizing data and analytics, Hawai’i Pacific Health gained a deep understanding of its supply chain processes and data, allowing it to improve and maintain the reliability of this information, leading to meaningful and sustained improvements across the system.
Pediatric sepsis remains a key concern for hospitals due to the serious nature of the disease. Early diagnosis and timely care are a top priority, as this significantly improves a patient’s chance of recovery. With the help of big data and prescriptive analytics, Texas Children’s Hospital developed an early alert system and workflow changes to improve its pediatric sepsis care. The hospital’s investment in new processes, decision support, and analytics has substantially improved pediatric sepsis outcomes.
Thirty percent of the entire world’s data is generated in the healthcare industry, with valuable information often locked in the EMR. For Orlando Health, the data required by operational leaders to effectively run emergency department operations were not easily accessible. By utilizing its analytics platform, Orlando Health leadership has expanded access and visibility to data to drive improvement efforts.
Community Health Network (CHNw) was keenly aware of the impact that opioid prescribing patterns have on potential opioid misuse and set a focus on decreasing opioid prescriptions; however, it lacked access to meaningful data that could be used to understand the volume of opioids that were prescribed postoperatively. CHNw created an orthopedics guidance team and leveraged data within its analytics platform to gain insight into prescribing habits over time.
Improving the management of chronic diseases for patients is crucial for reducing expenses and improving health outcomes. Newton-Wellesley Hospital, a member of the Partners HealthCare system, adopted the population health coordinator role and utilized analytics to help identify variations in chronic disease management across practices and develop standardized best practices aimed at reducing costs through better outcomes for patients.