Looking for a way to share his extensive experience with data warehousing in healthcare, in 2002 Dale Sanders wrote what many consider to be the “EDW Bible.” It’s a document with guidance that, if followed, will drive value and utilization from a data warehouse. We’ve made that report available now.
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Dr. John Haughom explains 5 key Deming processes that can be applied to healthcare process improvement. These include 1) quality improvement as the science of process management, 2) if you cannot measure it, you cannot improve it, 3) managed care means managing the processes of care (not managing physicians and nurses), 4) the importance of the right data in the right format at the right time in the right hands, and 5) engaging the “smart cogs” of healthcare.
Health Catalyst is excited to announce that it will soon release the Surveillance Module of the Patient Safety Monitor™ Suite, the industry’s first comprehensive patient safety application to use predictive and text analytics combined with concurrent clinician review of data to help monitor, detect, predict and prevent threats to patients before harm can occur.
The Patient Safety Monitor™ Suite leverages AI and machine learning to quickly identify patterns of harm, learn from those patterns, and suggest strategies to eliminate patient safety risks and hazards. This potent combination of AI, machine learning, text analytics and near real-time data from multiple IT systems enables the Patient Safety Monitor™ Suite to predict harm events and guide clinical interventions while the patient is still in the hospital.
In this webinar you will learn how the Surveillance Module can provide:
- Greater clarity to the types, numbers, and causes of adverse events, enabling leaders to quickly prioritize improvement efforts
- Improved patient outcomes such as reduced morbidity, mortality, and length-of-stay, and increased quality-of-life and satisfaction
- Bottom-line cost savings and decreased brand damage related to unnecessary or preventable high-cost care and reduced/eliminated penalties
- The ability for clinicians and infection preventionists to focus on patient care instead of burdensome manual data extraction, aggregation, and reporting
In January, CMS announced the Bundled Payment for Care Improvement Advanced “BPCI Advanced” program, initiating renewed interest in a total cost of care payment model for specific episodes of care. Regardless of your organization’s current decision to participate, it’s important to understand how bundled payment programs have the ability to significantly decrease your internal costs, broaden your revenue opportunities, and improve patient outcomes across specific populations.
The Center for Medicare and Medicaid Innovation’s newest iteration of bundled payments provides another tightly-defined program that allows organizations to scale Population Health Management. Best practice suggests that tactical interventions to assess clinical variation, implement strategic care redesign programs, and to adjust care management-facilitated patient stratification models are important to be successful with bundled payments – so knowing how to implement them is crucial.
One organization’s savings is another’s income and without making overhead allocation changes, bundled payments may reduce revenue that has been critically important to maintain hospital profitability.
Join this webinar to learn:
- What is new with bundled payments
- The ramifications bundles can have across organizations
- Leveraging data and strategic analysis to identify opportunities for bundled payment success
- Operationalizing successful care program tactics to be successful in bundled payment contracts
Many healthcare organizations struggle to deliver seamless reporting, advanced visualizations, and end-user self-service models, but these types of analytics are critical to business intelligence and have become a practical and strategic necessity. There is a lack of trust in data because it can be difficult to access and combine information that is fragmented, coming from multiple, disparate sources such as EMRs, billing, claims, and financial systems. Without an integrated source of clinical and business data in a trusted single source of truth, it is difficult, if not impossible to create a data-driven approach to decision making.
Orlando Health, one of Florida’s most comprehensive private, not-for-profit healthcare networks consisting of eight hospitals and 50 clinics, began its journey to integrate clinical and business data into a single source of truth across the organization when it made the transition from a legacy data warehouse solution that employed an enterprise data model to the Health Catalyst analytics platform, and subsequently to the Health Catalyst® Data Operating System (DOS™) platform.
Integrated data and ability to deliver superior solutions has resulted in a single source of truth, leading to increased adoption. Once customers realized the timeliness, ease of access, and quality of the improved analytics and visualizations available to them, demand and adoption increased and continues to grow.
- 6,120 queries of the analytics platform each month.
- Users access analytic applications and visualizations more than 700 times each month.
Changes in payment models are putting pressure on clinicians to close gaps in care. To do this, they need instant access to actionable information about their patients and their own performance. However, many electronic health records and business intelligence systems are still grappling with how to deliver the insights necessary to revolutionize the way providers work.
Orlando Health, a Florida-based, not-for-profit health system made up of eight hospitals and 50 clinics, found its enterprise data model difficult to scale, making it challenging to gain insights from its healthcare data. Building upon its analytics platform, Orlando Health recognized the value of immediate access to adaptive, integrated healthcare data that could be rapidly deployed in consumable, actionable visualizations to address a wide spectrum of business needs and use cases, and embraced a next-generation data model.
- Ten data sources loaded into the platform in under six months.
- As little as one week to deploy dashboards, visualizations, and analytic insights.
- 95 percent reduction in work hours required to incorporate system enhancements.
- 88 days saved in the amount of time required to implement system enhancements.