Patient Safety and Quality of Care

The delivery of quality care to patients is the core business of any health care provider organization. But nonintegrated legacy systems and staffing shortages can result in deviations from standards in the delivery of care to patients.

Consequences may include: Unfavorable clinical outcomes. Increased cost of care. Higher malpractice costs. Inappropriate use of resources. Loss of reputation. Loss of accreditation.

SAS can help you effectively manage quality of care and drive sustainable improvements by measuring and viewing clinical performance, resource utilization, cost-effectiveness, pathway development and evidence-based decision making.

Quality Improvement
What if you could improve your care processes by analyzing and predicting variances in care?

SAS supports both analysis for variance identification (e.g., predicting the response to changes in treatment protocols for ventilator patients) and structured analysis (e.g., analyzing readmission rates by DRGs). SAS analytics use unstructured methodologies to discover hidden patterns without a predetermined idea or hypothesis about what the pattern may be. From desktop applications that support clinical quality teams to sophisticated enterprisewide performance management systems, SAS analytics come in all levels of sophistication, enabling users to:

• Uncover unexpected patterns or rules for use in effecting evidence-based improvement in clinical and support operations.
• Use analysis of variance to demonstrate compliance with professional standards of practice, accreditation requirements and the documentation of patient satisfaction.
• Identify and investigate key drivers of deviations in standards of care delivery across care settings.
• Analyze trends and patterns in clinical errors. Identify and eliminate patient care processes that lead to Sentinel Events.

Creating a true evidence-based culture requires health care providers to empower management and staff with analytic and knowledge management capabilities for strategic decision making that will lead to:

• Increased patient safety.
• Improved quality of care.
• More effective risk management practices.
• Better application of best practices for care protocols.

Organizations that focus on giving physicians information that they can use to improve care will position themselves for the long term by gaining not only physician trust and loyalty, but also increased market share. SAS data management, analytic and reporting solutions enable you to uncover that kind of information – and empower physicians to make evidence-based changes to practice patterns – by:

• Consolidating data for measuring and reporting practice and prescribing patterns.
  
• Tracking by specific disease, population, DRG, etc.
  
• Surfacing physician measures to monitor cost, use and quality at a high level, while also allowing drill-down to more detailed data.

Utilization Analysis & Reporting
What if you could clearly understand the demand for services at various times and under various conditions?

SAS solutions give you the ability to assemble highly sophisticated predictive models that can analyze and predict such utilization variables as:

• Demand for future services by geography, medical need and payer type.
• When future need will occur (including time of day and, more importantly, the point in time during the patient’s health history).
• How future encounters will take place (emergency, routine visit, hospital admission, etc.)
• Financial encounter projections, including how much revenue future encounters will produce, payer type, what it will cost, etc.

• Revenue prediction – Identifying the sources and timing of revenue from patients, health plans, employer plans, pay-for-performance programs, etc.
• Cost prediction – Placing the right resources in the right place at the right time and uncovering where wasteful expenditures occur.
• Investment decision support – Making evidence-based decisions, such as type and location of facilities, type and amount of equipment, etc.
• Knowledge and skills management – Understanding the characteristics of the anticipated patient population to support recruiting decisions, staff development decisions, training program content, etc.