~ Co-authored by Gail Bamford, David Wallace and Mike Newkirk ~
According to the World Health Organization, global health spending totalled more than US$ 4.1 trillion in 2007, with US$ 639 as the total health expenditure per person. That number will only grow in ways that affect businesses and citizens.
Despite these huge investments, health care quality is uneven and resistant to changes and improvements. How can we enhance health care delivery while controlling those costs? It starts by carefully measuring and monitoring the quality of that care – a complex task perfectly suited for business analytics. Here’s how some forward-thinking health care institutions are delivering better quality of care more efficiently.
Maine Medical Center
Named to U.S. News and World Report’s “America’s Best Hospitals” list for orthopedics, heart care, and gynecologic care, Maine Medical Center uses SAS Business Analytics to understand key patient care metrics – and sustain a quality-driven culture. The data-driven approach has produced excellent results:
- Increased compliance on medication reconciliation by more than 50 percent in a nine-month period.
- Dramatically reduced the rate of hospital-acquired infections by measuring where infections originated and what admission conditions closely correlated with acquired infections.
- Improved government/industry accreditation/compliance by incorporating national guidelines into key metrics.
- Developed new methods for caring for stroke patients while controlling costs. By taking better care of these patients, the hospital expects fewer complications, which will reduce costs.
The Karolinska Institute in Sweden needed a way to examine the effects of drugs, other treatments and lifestyle factors on patients with rheumatoid arthritis. Using SAS Business Analytics, the Institute has deployed a Web-based patient self-help application and predictive modeling to determine which treatments will be most effective for certain segments of RA patients.
In a challenging economic and regulatory climate, bankers must be especially vigilant. Two key indicators of a bank’s health are net charge-offs (NCOs) – the value of loans written off as uncollectable – and nonperforming loans (NPLs) that are in default or delinquent more than 90 days.
In the past two years in the US, bank NCOs have soared by an average of more than 350 percent across all institutions, with institutions holding assets of $5 billion or less showing growth of almost 500 percent. NPLs as a percentage of average loan balances have risen more than 278 percent at US banks with $1 billion or more in assets. 1 How can financial institutions improve their collections and protect their bottom line?
Business analytics can provide the insights that institutions need to reduce both loan writeoffs and the cost of collections activities. First, models created within a business analytics framework can identify likely candidates for workouts and loan modifications. Second, business analytics can optimize collections activities to improve the probability of success and maximize self-treatment among debtor segments. It starts with three basic steps.
- Cleanse and integrate – Cleanse and standardize third-party credit and customer data, enrich it (e.g., add geocoding tags), and integrate it into a single data store.
- Analyze and score – Develop scoring models to analyze debtor-customer segment data against objectives, including “maximize profits” or “minimize writeoffs” or against constraints, such as loan types, outstanding balances, or days delinquent.
- Optimize and execute treatment strategies – Analytical models help collections teams understand who is most likely to respond, which communication channels work best, and how much payment to expect.
Collections optimization driven by business analytics delivers the results that institutions need to improve their profitability.
From diapers to jet engines and almost everything in between, manufacturing expertise is a competitive differentiator for companies that follow optimal practices and methodologies to attack inefficiencies and eliminate waste. Business analytics is essential in these settings to improve production and sales planning, enhance the supply chain, reduce inventory, streamline logistics and much more.
For example, with demand forecasting, business analytics can be a key contributor to a manufacturer’s success. Better forecasts deliver ROI by:
- Reducing inventories.
- Improving order fulfillment rates.
- Shortening cash-to-cash cycles.
Many manufacturers struggle with optimally managing and forecasting their raw materials requirements, work-in-process (WIP) inventory and finished goods inventories. Without the right mix of raw materials, production plans fall apart and customer orders are delayed (or, worse, canceled). Missing WIP forecasts similarly leads to inefficient schedules and a crippling misallocation of finished stocks – not having the right quantities of the right goods at the right time and in the right places. While the data is often available to prevent, identify and correct these imbalances and inefficiencies, it is usually not integrated, analyzed and shared across the organization.
Data management technologies can bring together islands of information such as point-of-sale (POS) data and historical shipment data. Once that data is aggregated, business analytics models and tools can accurately forecast the demand for products by family, individual SKU, geography, customer type, etc. With a clear and accurate demand picture, manufacturers can properly allocate raw materials across plants and regions – all optimized by distribution channel – to create complete roll-ups in master planning schedules.
You’ve likely experienced it before – your cell phone loses service one too many times, so you switch providers. Low barriers to churning mean providers must vigilantly and carefully invest to maintain and increase their service quality and customer satisfaction rankings. After all, your satisfaction keeps them in business.
Network managers typically receive error reports and alarms after a network device fails. The team addresses the stream of trouble tickets, but never gets insight into underlying causes or trends for outages. The result: long call-resolution times.
With business analytics and approaches such as predictive fault analysis, network managers can analyze performance to pre-empt failures. They can analyze trouble tickets and optimize corrective services, shortening times you are without coverage.
Strong data management, including data quality and reporting capabilities – all key underpinnings for business analytics – can help quickly identify service and network issues. Business analytics helps to:
- Identify and remove duplicate trouble tickets.
- Understand faults and performance on a macro level.
- Determine which services have the highest fault rates.
In addition to analyzing network performance, predictive analytics technologies can help evaluate demand, faults and systems to improve resource utilization and quality of service (QoS). A telco provider can then identify when and where network resources are deployed and quality/performance variations over time.
Business analytics allows network and service managers to better understand causes and impacts of failures. They can prioritize and pre-empt outages, optimize repairs and mitigate risk with answers to key questions:
- How significant is each factor influencing network faults/degradation?
- Which network faults are tied to a given trouble ticket?
- Which faults are related and what are their impacts?
Armed with predictive fault analytics, a telco provider can limit the times you lose a signal and continually improve overall service, allowing it to keep your business.
This article was reprinted from the SAS Insights Report, Brain trust: Enabling the confident enterprise with business analytics.