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How To Use Analytics to Create Health Care Value: An Executive PrimerUse analytics to fish for insights hidden in your data lake

About the Author

Mike McGuire is a Health Care Analytics Adviser in SAS’ Health and Life Sciences business unit, where he helps health care executives achieve their organizational and professional goals, respond to health care reform, drive improvement, and create patient and organizational value. You can contact him at mike.mcguire@sas.com or www.linkedin.com/in/michaelemcguire.

ContentsThe Bait: Valuable Health Care Insights .......................1

More Than a Fishing Pole: The Analytic Process of Creating Value ...............................................................1

Where Are All the Fish? Barriers to Creating Value With Analytics ....................................................................2

Unproductive Analysts ..........................................................3

Expensive Infrastructure Growth ........................................4

Deployment Challenges ......................................................4

Maintaining Trust in the Analytic Answers ........................4

Load Your Tackle Box With a Variety of Lures: Addressing the Barriers ...................................................4

Design Your Tackle Box: Create a Vision for Your Analytic Strategy ................................................6

Learn to Fish: Grow Your Analytic Maturity ..................7

Go Fishing! Create Value From Your Data ...................7

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The Bait: Valuable Health Care InsightsAnalytics is hot, and for good reason! It’s hard to find a magazine that doesn’t feature articles about analytics because businesses are learning about its potential to unearth valuable insights. And no other industry today sees a greater potential to use analytics to fish for insight from their growing data lakes than health care.

If you believe analytics will help your organization, the question becomes, “What are the critical executive considerations for applying analytics strategically?” Let’s use a simple fishing metaphor: How do you go fishing for health care value?

More Than a Fishing Pole: The Analytic Process of Creating ValueWhat is analytics, really? Analytics is often regarded as a range of capabilities to answer both general and specific questions based on facts derived from data. Figure 1 shows an example of the progression of questions related to diabetes that can be answered through standard reports, ad hoc reports, query drill-down, alerts, statistical analysis, forecasting, predictive modeling, and optimization – with each providing increasing levels of depth and multifaceted analyses.

Optimization

PredictiveModeling

Forecasting

StatisticalAnalysis

Alerts

QueryDrill-Down

Ad HocReports

StandardReports

What options provide maximal value right now for this patient?

What impact can we expect from outreach?

What will diabetic care cost next year?

Who is at highest risk?

Which patients have gaps in care?

Which physicians have better performance?

How many diabetic patients live in a specific ZIP code?

What percentage of diabetic patients have had a recent HbA1c test?

Data Information Knowledge Intelligence

Hea

lth

Car

e V

alu

e

Degree of Intelligence

Figure 1. The evolution from hindsight reporting to predictive analytics in the case of diabetes analysis.

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To realize value, think of analytics as a process. It’s good for answering questions like those above, but it’s much more than just a tool used to fish for answers.

Embedded as an ongoing business process, analytics can turn your organization into an agile, learning entity. The analytic process can become the mechanism to enable learning and insights from all your data assets – corporate and external. It can be the foundation upon which your unique health care organization, your unique patients/members, and your unique value can grow – financially and intellectually – organically and inorganically. All organizations learn, but do they learn quickly enough to

survive? Never before has health care had to learn to respond to change faster than today. To be a learning health care orga-nization that flourishes, the analytic process must be repeatable and scalable with long-term, sustained executive commitment. Like any other organizational strategy designed to deliver significant results, analytics requires a major commitment to achieve success.

In the past, organizations have often taken tactical approaches to analytics focused on specific projects. While this approach can add limited value, you must have a strategic analytics plan to create broader organizational value. You need a master plan to make sure the tactical steps are additive and move toward a unified analytic vision to create value.

Do you have a strategic plan to find all the fish in your data lake?

Where Are All the Fish? Barriers to Creating Value With AnalyticsThe real promise of analytics in health care is predicting outcomes, making prescriptive recommendations, and opti-mizing administrative and clinical processes. With this insight, you can create better outcomes, better results and a better future. However, there are hidden barriers to a better future, and many of these barriers often leave executives wondering, “Where’s the real value?”

Low analyst productivity, expensive infrastructure growth, deployment challenges and lack of trust in the analytic insight all inhibit value realization.

Embedded as an ongoing business process, analytics can turn your organization into an agile, learning entity.

Figure 2. Analytics as a process can be approached in four phases: Manage and Discover, Model, Deploy and Monitor.

Manage and

Discover

Deploy

ModelMonitor

AnalyticProcess

As seen in Figure 2 at its most basic level, the analytic process:

• Manages data and reveals insight.

• Models the insight to better understand what will likely happen in the future.

• Deploys the insight to support decisions that will create better outcomes.

• Monitors the results.

When your organization follows this process, your organiza-tion learns.

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Unproductive AnalystsThe analytic process requires people, software and infrastruc-ture, but of the three, people are the most important. The people component can be even greater, but a good rule of thumb is that an analytic platform is 60 percent people, 20 percent software and 20 percent infrastructure when hidden costs are included.1 Although it’s obvious that people are the largest component, the financial view actually undervalues the people. People do the fishing using their knowledge of your organization and your data along with their available analytics capabilities to develop valuable insight.

• Updating reports that could be updated by the user enabled with self-service.

• Limited analytical approaches available to create the most accurate prediction.

• Waiting on jobs to run.

• Slow iterations through analytical models during refinement.

• Manually considering multiple models.

• Manually converting models into deployable code.

• Testing the code.

• Manually monitoring the results.

• Maintaining previously deployed analytical models.

Every moment lost is a missed opportunity to find new insight, deploy the insight more quickly, and create organizational value from the insight. Automation is key. Every step in the analytic process that is automated will save a portion of your biggest investment in analytics – your people.

Two areas of caution:

• Although your analytics team is critical, be careful to avoid depending on a single individual. To scale, an analytics team needs to be able to share its work, back up other team members, replace anyone who leaves, and grow capacity over time. Productivity must be approached at the team level and supported by automated capabilities. Although a single individual may be able to take many disparate analytic tools and complete an analytic task, that task is rarely repeatable by the broader team, and as such is very difficult to accom-plish and support.

• Also resist the temptation to outsource your analytics team, since it is the most critical part of creating analytic value. An analytics team isn’t interchangeable with a team of consul-tants with only general health care knowledge but no specific experience with your organization or with your patients/members. A better approach is to augment your team with experts who help deliver value while developing your team’s capabilities. Create a plan that reduces the involvement of outside experts over time as your internal team ramps up to institutionalize your analytics expertise.

Focus on approaches that will improve the productivity of the entire analytics team throughout the analytic process. Make everyone’s fishing efforts more worthwhile.

1 Total Cost of Ownership TCO Explained, Solution Matrix Ltd., www.business-case-analysis.com/total-cost-of-ownership.html#model.

Figure 3. Typical cost allocation of an analytics platform.

0 10 20 30 40 50 60

People60%

Infrastructure20%

Software20%

Often the first question is how many people to hire. While this is a reasonable question, what’s more important to consider is the productivity of the people engaged in your analytics strategy. Thus, it’s better to strategically focus on maximizing the productivity of your people – your core analytics team and analytics users throughout your organization – who will be doing the fishing.

What issues affect the productivity of an analytics team? Several:

• Waiting on data.

• Manual processing of data to prepare it for analytics.

• Working around gaps within tools.

• Working with nonintegrated tools – and filling the holes between them.

• Answering questions that can be answered by a user provided with self-service capabilities.

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Expensive Infrastructure GrowthAs organizations plan their analytics strategy, often infrastruc-ture is viewed as a commodity component. It is less important than people, but often the cost of growing infrastructure will delay projects and their associated value. Hadoop is an open source technology that promises to significantly improve the price performance of your analytics infrastructure. Make sure your infrastructure includes Hadoop, or you can easily add Hadoop to hedge against infrastructure costs delaying value creation over time.

Deployment ChallengesIn order to create substantial value from analytics, it is important for decision makers to understand and believe the insights were developed through a structured analytics process; they must trust the insight to use it. The predictive insight needs to be available at the right time, in the right format and to the right person. For large, one-time decisions, this may be relatively simple. The analytical result can be directly communicated by the analyst to executive decision makers. The executives can ask questions to personally validate the prediction – to decide if they trust it enough to use as a basis for their decision-making process. However, this approach will fail to support a large number of decisions or a large number of decision makers.

Today, organizations outside of health care are using analytics to make day-to-day decisions. In fact, implementing any corporate strategic direction typically requires day-to-day decisions throughout the organization to achieve the desired goal. It may even require decisions at key points of interaction:

• When a doctor is seeing a patient.

• When a call center representative is speaking with a member.

• When an operating room is being scheduled.

• When a member of a managed population considers preventive care.

Therefore, it’s important to envision automated deployment of analytic insights in your strategy so you can catch a lot of little fish and make a big difference.

Maintaining Trust in the Analytic AnswersTo maintain trust over time, analytic predictions must be moni-tored for continued accuracy, revised to address changing conditions, improved to use newly available data, retired when no longer applicable, and replaced with more accurate approaches. Decision makers’ trust in analytics is directly related to how effectively analytic results are monitored for continued accuracy. Unless this monitoring process can be automated, analytic value will be constricted by your analytic team’s capacity to manually manage analytic models in production. Trust must be maintained as you scale your analytics, so include plans for automated monitoring in your strategy.

Load Your Tackle Box With a Variety of Lures: Addressing the BarriersIn a very real way, analytics lives at the intersection between operational systems (EMRs, supply chain, care management, quality, etc.) and productivity systems (Microsoft Office, Excel, PowerPoint, etc.). Before the proliferation of strategic analytics, executives often thought of analytics as a set of discrete capa-bilities without much thought to the entire analytic process. Today, organizations that are strategically approaching analytics have recognized that the steps in the analytic process are interconnected, and the lack of integration can significantly affect the degree of manual work – and thus the overall orga-nizational value. They have recognized the need to implement their analytics environment as an integrated platform that can interact with both their operational and productivity platforms – i.e., an analytics platform.

An analytics platform, like operational and productivity plat-forms, provides an integrated environment that will support the entire analytic process from managing data and discovering insight to monitoring the results. The platform must maximize the productivity of your analytics team, easily grow and be supportable by your IT team. Also, it must easily interoperate with the operational and productivity platforms throughout the analytic process.

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Interoperability is critical in two of the four steps in the analytic process. To manage and explore data, the analytics platform must be able to quickly access operational data and prepare that data for analytic work. Most organizations have a variety of operational systems; therefore, strong capabilities to interact with a wide range of data sources are important contributors to the analytic team’s productivity.

Deploying the analytic insights is the other critical integration point between platforms. Deployment requirements will vary depending on the characteristics of the analytic insight and the depth of deployment. Often deployment starts with providing reports and dashboards. Here, integration with the productivity platform can be a major benefit to your analytics team. An analytics platform should allow your organization to interact with live, updatable analytics through Microsoft Office, Excel, PowerPoint and SharePoint. Analytics effectively embedded in the natural workflow of your productivity platform becomes much more accessible, consumable and useful.

Deploying analytics often requires interaction with operational systems to deliver predictive insight into the business or clinical workflows. An analytics platform should provide many opera-tional deployment options, ranging from scoring an analytic model in an operational database to interacting with business rules to real-time analytic scoring. These capabilities include the ability to automatically turn an analytic model into software code that can run in the operational environment, saving time otherwise lost with manual coding, debugging and testing. Without the ability to interoperate with operational systems, full organizational value from analytics will never be realized.

In other words, bring a full tackle box if you want to catch all the fish!

Manageand

Discover

Deploy

ModelMonitor

AnalyticProcess

Operational Platform

Acute Care

Supply Chain Mgmt

Ambulatory Care

EMR & Others

RiskACO/PHM

Human Resource

Management

Analytics Platform

Productivity Platform

Figure 4. The organizational ecosystem of operational, analytics and productivity platforms synchronized for access and deployment to create maximum value.

Interoperability is critical in two of the four steps in the analytic process.

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or it can start with targeted capabilities and add both capabili-ties and capacity over time. Either approach will work. The first approach aligns better with an enterprise analytic strategy. The latter aligns better with a project-focused analytic strategy, adding more capabilities to support specific projects as they are identified. The key is to envision a fully capable analytics platform with a road map that creates broad organi-zational value.

You never want to have to stop fishing because you run out of fishing tackle!

Design Your Tackle Box: Create a Vision for Your Analytic StrategyA breadth of capabilities are available through a health care analytics platform, as shown in Figure 5. The platform lives in a secure environment and has access to both internal and external data. Analytic “apps” can be surfaced and users can collaborate through them. With controlled security, any user can access any capability provisioned for them. Data can be exported to operational and productivity systems as well as to third parties such as regulatory agencies.

An analytics strategy should include an analytics platform with the capability to expand to this scope. The analytics platform can start with all the capabilities and scale capacity over time,

Secure Cloud

Any Output DestinationInternal/External

Any Input SourceInternal/External

Any Customer, Channel and Device

Insights

Export

Data

Any Service Any Vertical or Horizontal

Slice of Stack

✓ Patients✓ EHR ✓ Call Center✓ Visualization✓ MS O�ce✓ Regulatory Reporting✓ Partners

Patients

Clinicians

Managers

Analysts

IT Sta�

Third-Party App

Reg/Adt EHR Lab Billing OtherPatient Sat.

Users

Social CommunityUI, Authenticate, Submit & Request Data, Navigate &

Access Applications, Collaborate & Share Insights

Packaged Analytic Applications & Useful InsightsPredictive Models, Benchmarks, Actions/Alerting – Clinical,

Administrative, Operations, Financial , Quality, Gaming Theory

Analytic Tools FoundationData Connectivity, Data Quality, Visualization, Segmentation,

Data Mining, Forecasting, Audit Trails, NLP, Machine Intelligence

Storage & DataBladed Environment – EDW, ODS, Marts, Hadoop + Customer Data,

In-Memory Databases, Virtual Data Marts

Figure 5. The health care analytics platform offers a breadth of capabilities across the organization for many user skill levels and roles.

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Value-Based Health Care

Manage Financial Risks and Incentives

Proactively Manage Quality and Outcomes

Improve E�ciency of Care Delivery

Population Health and Engage Patients

New Business Opportunities/Growth

Establish Comprehensive Analytics Foundation

Go Fishing! Create Value From Your DataThe right team with a highly-productive analytics platform will support the strategic application of analytics within your orga-nization. With each successful project, your analytics team will help your organization become more agile while continuously learning from your data. With each successful project, more data will become available so users throughout your orga-nization can independently answer simpler questions while collaborating with your analytics team on more complex and valuable questions.

Over time, your organization will come to have more and more trust in analytic insights. It will trust that multiple analytic approaches have been considered, and the best prediction or recommendation has been found. It will trust the analytic opera-tional insight to support day-to-day processes to create better outcomes and results.

Organizational trust makes analytics valuable, but it takes more than just an algorithm to create trust.

With the right analytics platform, your analytics team will teach the rest of your organization how to fish!

For more information, visit sas.com/healthcare.

Learn to Fish: Grow Your Analytic MaturitySimilar to the analytics platform’s positioning between opera-tional and productivity platforms, the analytics team will work at the intersection between data and organizational knowledge – creating both clinical and financial value. Given the future demands for value-based health care, an analytics team will likely focus on challenges relating to:

• Managing financial risks and incentives.

• Proactively managing quality and outcomes.

• Improving the efficiency of care delivery.

• Managing population health and engaging patients.

Using analytics to create value in these areas will be a learning process for the analytics team and the broader organization. Since analytics is relatively new to health care, many team members may come from other industries that are less complex than health care. As the depth of your team’s health care under-standing increases, so will the analytic value it creates. As the team learns to fish, it will become progressively more effective and productive.

Figure 6. The health care analytics platform contributes to the goal of value-based health care on several fronts.

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