Hospitals, data and analytics: What the leaders think

Everyone in health care is talking about data and analytics these days and the vendor world has shifted into overdrive. So what is really going on in the trenches, and what does this mean for the future? Does this mean that we will finally find the holy grail of information-driven care?

Health and life sciences experts at SAS wanted to better understand how health care organizations are using and sharing their data. With the assistance of IDG Research Services, we conducted a Quick Pulse survey on health care data and analytics practices during March 2016.

About the survey

In order to participate in this six-question, online survey, respondents must be at a director-level or above and work for a health care institution with one or more hospitals and beds for acute inpatient care and at least 500 employees. The survey explored:

Areas or departments which have access to/use different types of data.

  • Progress with data integration.
  • Important data and analytics objectives.
  • Use of data scientists within their organizations.

The charts below show distribution of job titles and organization size, for the 54 respondents:

  cudney chart onecudney chart two

The importance of data and analytics for patient care and engagement

In several ways, the survey results tell us that a lot of work related to data and analytics is underway. On the other hand, the results tell us that they’re not there yet.

The table below compares objectives versus practices for data and analytics in patient care and engagement. The majority of respondents highly rated the importance of each objective, which underscored the priority placed on data and analytics. However, in some objectives there is a significant gap in the importance ratings versus actual practices.

The survey revealed a dichotomy. Interdepartmental collaboration to use data for improvement was noted as Critical or Very important by 93 percent of respondents. However, only 74 percent believe this is Always or Usually True in their organizations. Similarly, 91 percent of respondents said the ability to personalize patient care and engagement using analytics is Critical or Very important. Yet only 76 percent believes this is Always or Usually True in their organizations.

Access to and use of data

Respondents emphasized that even though personalized care is a top objective, clinical teams are the least likely to have access to different data types outside of their specific area.cudney chart three

Similarly, respondents indicated that clinical staff are usually able to personalize patient care. However, these efforts may be based on incomplete data because organizations are inconsistent in ensuring that insights gleaned from data across all populations is easily accessible to patient-facing staff.

This highlights the continuing challenges with providing analytics-ready data at the point of service, where front-line professionals make clinical and operational decisions that affect lives and outcomes of patients and their families.

In Part 2 of this series, I’ll tell you about the progress that hospitals are making and sense of optimism about analytics leading the future.

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Dignity Health demonstrates the power of advanced analytics at HIMSS16

According to Lloyd Dean, president and CEO, "At Dignity Health, we are committed to developing partnerships and opportunities that harness the tremendous potential of technology, from improving the patient experience to providing caregivers with tools that will support their day-to-day care decisions."

Dignity Health, one of the largest health systems in the US, continues to demonstrate how its partnership with SAS and the use of advanced analytics can propel improvements in the delivery of health care. It recently presented several breakout sessions during HIMSS16 in Las Vegas.One of these sessions provides an excellent example of what Dean was talking about. Alyson D’Andrea and Ken Ferrell, representing the Dignity Health Insights (DHI) team, presented, Sepsis Biosurveillance at Dignity Health: Our Improvement Story.

The high cost of sepsis

clinicalSepsis is the body’s overwhelming and sometimes life-threatening response to infection which can lead to tissue damage, organ failure and death. Patients who develop sepsis have an increased risk of complications and death and face higher health care costs and longer treatment.

The sepsis biosurveillance program represents the vision of the DHI team:

  • Use big data and the world's best analytic tools in a secured cloud-environment to improve health outcomes and reduce costs.
  • Use data across the continuum of care to improve outcomes of patients, increase knowledge, worker productivity, enable self-service access and markedly increase calculation speed and insights to decision makers.

During this session, D'Andrea and Ferrell explained the process for creating an internal dashboard for monitoring performance on several measures related to severe sepsis. Specifically, the tool and associated workflows implemented at 16 facilities (so far) helped reduce sepsis mortality rates. According to Dignity, an average of 69,000 people per month were monitored during this phase of the program.

For more than two decades, researchers and professional societies, including the American College of Chest Physicians (ACCP) and the Society of Critical Care Medicine (SCCM) have driven improved practice and consensus around severe sepsis.

Clinical leaders at DHI understood the importance of this initiative and looked for a way to use data from the electronic medical record with their SAS analytics platform to create a dashboard that shows the status on key performance indicators, allowing rapid intervention to save lives and improve the clinical outcomes of patients at risk for sepsis. This also supports generating evidence from actual practice to inform research and further refinement of evidence-based practice. This performance dashboard uses Hadoop technology, which significantly decreases storage cost, aggregates a variety of data sources, as well as enables enhanced security and flexibility in using the data.

Alerts and analytics

The DHI project strategy included identifying key performance indicators, implementing the St. John Sepsis Agent tool within the Cerner EHR system and creating a feedback loop to enable stakeholders to improve response time to the alert. The DHI team statistically validated the sepsis dashboard for accuracy and functionality. This included a "silent mode" evaluation, whereby manual chart review helped to correlate the St. John's Agent activity with patient discharge diagnosis. Subsequently, the team developed clinical alerts, carefully tweaking the sensitivity, specificity and relative risk to achieve just the right balance. Using Cerner’s alert, the team validated their findings and proposed design with internal stakeholders to ensure the result would achieve agreed goals for the project. A major focus of the DHI team was to direct the smarter alert to the person responsible for the patient at the time of the alert. This was defined as the primary care nurse, charge nurse, triage nurse and attending provider.

The team also worked to ensure the alerts fired at the right time, which was when the presence of sepsis in a patient was identified and 24-48 hours after the previous alert fired. The team also worked to ensure that the alerts filtered out patients, who were known to have sepsis at the start of their hospitalization. Alert filtering also included those who had cardiac surgery or extracorporeal membrane oxygenation (ECMO) within the previous 48 hours.

Better response, lower mortality

With initial implementation of the sepsis dashboard, the team soon began to see that higher nurse communication rates (nurses responding to alerts) correlated with lower mortality rates. They also were pleased to see fewer patients progressing to severe stages of sepsis. As with many improvement projects, they also discovered other opportunities for improvement such as achieving greater accuracy of clinical coding and clinician compliance with sepsis bundle orders.

As these programs continue, the DHI time will continue to focus on governance and goal ownership three or four meaningful key performance indicators, optimizing alerts around workflow and content and providing feedback to stakeholders in an actionable format.

Using its partnership with SAS, the DHI team has demonstrated a powerful use case for analytics in clinical care, and a strong argument for self-service visual analytics. Dignity Health has effectively enabled providers to better manage patient risk, decrease patient mortality, shorten length of stay in the intensive care unit and achieve overall reduction in cost of care. To learn more about the work at Dignity Health, please visit its website.

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Here comes MACRA: Analytics can help you measure and manage

Medicare payment changes are coming. The Centers for Medicare and Medicaid Services (CMS) has announced the intention of increasing the proportion of payments to providers based on outcomes and changes in health status, as opposed to delivery of services.

At the January 11th, 2016 J.P. Morgan Annual Health Care Conference, Jan. 11, 2016, CMS Acting Administrator, Andy Slavitt commented that Meaningful Use criteria will soon be replaced by outcomes-based measures. Specifically, he stated, "Now that we effectively have technology into virtually every place care is med_computer_MG_7104provided, we are now in the process of ending Meaningful Use and moving to a new regime culminating with the MACRA implementation." MACRA (Medicare Access and CHIP Reauthorization Act of 2015) makes significant changes to the way Medicare pays physicians, accelerating Medicare’s shift toward value-based payments.

Measures based on MACRA include: quality, cost, use of technology and practice improvement. MACRA also replaces the traditional sustainable growth rate model for reimbursing for serviced delivered by physicians. In addition, this new focus will "move away from rewarding providers for the use of technology and towards the outcome they achieve with their patients," according to Mr. Slavitt.

This is consistent with the January 2015 announcement of CMS to set an aggressive target for transitioning to Medicare fee-for-service payments that are linked to quality, as well as those based on alternative payment models, such as accountable care organizations (ACOs). This aggressive target means that in 2018, 90 percent of the reimbursements CMS pays for health care would be through those arrangements.

This unraveling of the longstanding link between delivery of services and payment for those services (fee-for-service health care) is forcing providers and care delivery organizations to think differently about their roles and assume a level of risk that few are prepared to handle.

Under the old way of thinking, hospital-based organizations would be less likely to promote ACOs because it would likely mean a decrease in demand for acute care as patient health improves. So, care delivery organizations are facing a transition that could impact their financial viability if it is not managed effectively.

In this situation, the old adage, "if you can't measure it, you can't manage it," still rings true. We seem to be drowning in a sea of digital data. Implementation of electronic health records is almost ubiquitous. Data seems to be everywhere but the ability to quickly harness the data, understand and analyze it, then turn it into actionable information is elusive.

Analysts and programmers can help, but creating meaningful reports takes time and resources, which are usually in short supply. The ability to understand and analyze the changes in value, will define the ability to manage risk and maintain margin in this emerging environment.

The power of information must be available to health care leaders and front-line professionals, using the power of their data, in real time, to explore diagnoses, interventions, responses and changes in health status.

Practitioners and leaders in care delivery organizations can no longer afford to be restricted to submitting requests for static reports and then receive those reports without the corresponding ability to explore the data and understand what is really going on.

The SAS Analytics Framework for Health Care incorporates unparalleled expertise in data management into powerful software that encourages discovery and promotes clear, understandable and versatile deployment of that information. Users can rapidly identify relationships between variables, test assumptions, develop hypotheses and create predictive models. These capabilities used to fall exclusively within the realm of programmers and data scientists but now are readily available to those who are in the trenches.

In short, health care professionals must know what works and what doesn't . . . now. The SAS Analytics Framework for Health Care uses the latest in analytic technology to give health care professionals the insights to make more effective decisions – for the patient and the business.

Be sure to visit the SAS booth at HIMSS16 to learn more about these new capabilities in health care analytics.

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2016: Revolutionizing the patient experience

Health care is undergoing exponential change, and this year we’ll continue to see the industry launch bold missions to improve the patient experience. With new types of data and technologies, the vision of making health care more personalized and proactive is becoming closer to reality.

In my new book “The Patient Revolution: How Big Data and Analytics are Transforming the Patient Experience,” light is shed on several innovations shaping the future of health care; some of which we’ll see advance in 2016:KT_Tailor_Combined_PP2 68

Personal health clouds
In the world of connectivity and digitization, personal health clouds push and pull data to and from your everyday devices; be it your phone, your wearable device, or even your refrigerator! It’s all part of the Internet of Things (IoT) phenomenon, and being able to connect all of the information related to your health will help you and those involved in your health to make better, more personalized health care decisions.

Machine learning
By creating big data health clouds and using machine learning technologies, we’ll be able to automatically predict and prescribe. Whether it’s making personalized diagnostics possible, or creating recommendation engines, like those of Amazon and Netflix, to recommend the best personalized feedback to healthcare consumers, we’ll see machine learning gain fresh momentum with the explosion of health data.

Behavior change platforms
Detrimental behaviors like poor medication adherence and smoking not only have negative health impacts, but also cost the health care system a fortune. We need effective ways to support healthy behaviors, and that challenge introduces exciting opportunities for data analytics. For example, using population health analytics will help health plans and providers to effectively engage and support individuals outside of the clinical setting, and analytics tools that help guide behavior, rather than simply track it, will give individuals the personalized guidance they need to create healthy behaviors.

I’m excited to see these innovations in action this year and hope you’ll read more about them in my book. Next week I’ll share more tidbits from The Patient Revolution and in the meantime, be sure to check out this video:

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The analytics-driven marketing evolution in life sciences

42-35013593PharmaVOICE recently published an article: Analytics Driven Marketing for the pharmaceutical industry and I was fortunate enough to be interviewed. The article discusses the increased use of advanced analytics to make better marketing decisions, the progress that has been made and the challenges that still need to be overcome.

Within the industry there has been a clear evolution over the last few years regarding the adoption of marketing analytics. The two primary areas of focus have been analytics-driven targeting and analytics-driven messaging.

Analytics-driven targeting

Conventional wisdom in the industry was to channel sales teams and marketing resources towards physicians that historically had been high prescribers (or “top deciles”) of a particular therapy– a strategy that was, and to some extent still is, embedded in the pharma culture.

There is a clear trend in which companies are now using predictive analytics to develop profiles of high-value physicians based on mining of sales, CRM, socio economic/demographic and patient longitudinal data to create a more robust identification of prescribing patterns so that they can better target the right physicians in the future. By better targeting we mean identifying physicians that have higher value and are much more likely to respond to sales and marketing activities.

Analytics-driven messaging

Having identified the right physicians, it is essential to deliver the right message. The article features Pfizer’s use of analytics to engage physicians with information that is more meaningful. Pfizer accomplishes this by identifying the combination of messages that are most useful to physicians by providing them with information that will drive optimal patient outcomes.

This example clearly show an industry that is starting to advance in analytical maturity by understanding (with deeper context) customer behaviors and preferences.

What’s next?

Developing these advanced analytic capabilities is a journey of discovery. The next leg of the journey is understanding who your target customers are and what messages resonate with them so you can better orchestrate your interactions.

And it’s a challenge because of the data silos that still exist between sales, marketing, medical affairs and IT. Interactions with physicians across these functional divisions takes place daily. Without having the ability to views these interactions across all of the silos, it is not uncommon for a physician to be contacted by the same company multiple times in the same week – even the same day – for a single brand. This lack of coordination can do more harm than good and could have physicians clicking the unsubscribe button, especially if this excessive interaction is combined with a lack of relevancy. By eliminating these data silos you can then create a holistic view of physicians across the enterprise.

The challenge ahead

Now that the industry has started to evolve in developing analytic capabilities, it should attempt to connect the dots across data silos using master data management (MDM) to develop unified physician data – a foundational step that is necessary when you are faced with the type of problem described above. For example, you will have instances where a physician’s identity and other details are expressed differently in each silo leading to the question – is it the same physician or a different physician? MDM holds the answer. MDM maps out and unites all of these different identities and creates one golden record that is used for all marketing interactions.

Many life sciences companies looked at implementing MDM capabilities for the Physician Payments Sunshine Act, a regulatory initiative that requires more accuracy in physician identification.

Now that life sciences companies are starting to move up the analytical maturity curve, tackling MDM to better serve and provide more relevant marketing to their physician customers will be a more important capability as you continue on your analytical journey.

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Creating the care we deserve

Earlier this month I attended my first Forbes Healthcare Summit, where the stated goal for this year was to “figure out how to create the kind of care we know we deserve.” The event offered a unique gathering of pharmaceutical, health care and biotechnologhealth carey chief executives engaged in frank discussions on how to address the industry’s reputation, controversies over drug prices and the never-ending quest for new drug therapies.

Many of the speakers from the pharma, health insurance and provider industries are SAS health analytics customers, and I was struck by the increasingly important role that analytics is playing in their efforts. As we look to the new year, here are several takeaways (based on summit discussions) that will be key areas of focus in health care and life sciences for 2016.

Better care at lower cost. Changing the way health care is paid for in the US, with a greater shift toward improved health outcomes at lower costs, is a priority for executives today. We’re continuing to see health care reform drive value-based payments and reimbursements that affect an organization’s financial health. Because 85 cents of every dollar in health plans is required to be allocated to health delivery, understanding costs and variation in care delivery is critical to survival in this new environment.

Analytics is driving transparency of variations in cost, outcomes and provider performance that will serve as the baseline for transforming our health plan and provider customers’ business models.

Consumer choice changing market. Now more than ever, consumers are making choices about their health plans and services. Today’s consumers must become more educated in their care options, and the responsibility of those decisions is slowly transferring from the provider to the patient. This is an especially difficult transformation given the long history of doctors and health plans providing plans of care that patients followed without question.

Analytics plays an important role for our health care and life sciences customers in identifying specific consumer, patient and member groups and providing appropriate information, product choices and treatment options. Insights generated from analytics will drive innovation in clinical research, insurance options and patient choices.

Managing massive amounts of data. The volume of clinical and claims data continues to increase, along with the desire to incorporate other, nontraditional sources (e.g., wearable, lifestyle, social and demographic data) to further understand consumers, improve health care delivery processes and create a positive consumer experience. Developing and implementing strategies to understand patient behaviors, engagement preferences, and compliance to treatment protocols all begins with the data and the capability to deliver useful information and insights. IT organizations today have a tall order – capture, store, govern and manage volumes of critical data to enable providers and consumers with the information needed to make important care decisions.

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Health care spending waste? Analytics is the answer

142535507We have all heard many times about how much the US spends on health care each year. But let’s hear it again . . .  because it is staggering: According to Centers for Medicare and Medicaid Services (CMS), in 2013, the national health expenditure (NHE) grew to $2.9 trillion. That is nearly $10,000 per person and a whopping 17.4 percent of US GDP. Several estimates expect it to grow more than 5 percent year over year for the next 10 years. If that holds true, in 2025, the US will spend nearly $5.5 trillion on health care.

That's a lot of health care. Further, this will outpace GDP growth putting a bigger strain on an already huge problem. Is the US a particularly unhealthy country or are we doing something wrong?

It turns out we are doing something wrong, a lot wrong in fact. A large number of estimates put the percent of waste in the US health care system at 30 percent. This means that in 2013, about $870 billion was effectively wasted. But what does that mean exactly, and where are these dollars going when they are wasted? A 2012 article in the Journal of the American Medical Association outlines six key categories of waste in the US health care system:

Pricing Failure: This is indicative of a dysfunctional market and occurs when the prices for drugs and other health care services drastically deviate from those in better functioning markets. An example of this is when prices for drugs in the US run 30 to 100 percent more than the same drugs in Germany and the United Kingdom. Consumers of health care in the US rarely see the price leading to the dysfunctional market. The JAMA article estimated this portion of waste to be about $140B.

Fraud and Abuse: Fraud is essentially unauthorized benefits or payments while abuse is medically unnecessary or improper care. Estimates put this type of waste at about $200B.

Failure of Care Delivery: A good example of this is poor execution of preventative care that results in a worse clinical outcome and higher costs, such as not receiving a routine vaccine and then getting very sick. Estimates put this type of waste at about $130B.

Failures of Care Coordination: This is essentially fragmented care that results in significant inefficiencies. For example, there exists weak communications between outpatient and inpatient facilities resulting in ineffective or inappropriate care. Estimates put this type of waste at about $40B.

Overtreatment: This type of waste occurs when patients receive “defensive medicine” or excessive diagnostic tests. Defensive medicine is when practitioners order unnecessary tests out of fear of being sued for malpractice. Overall, this type of wastes accounts for about $200B

Administrative Complexity: This reflects the overly bureaucratic processes that reside in the US payer ecosystem. More specifically, this refers to the amount of time a practitioner would spend interacting with a payer versus actually treating patients. The estimate for this type of waste nearly $300B.

How can analytics help with each type of waste?

Pricing Failure: The trend toward a consumer driven marketplace in health care should dramatically impact the multiple types of pricing failures as consumers of health care will make tradeoffs based on costs. Analytics can help payers better engage this community to explore, understand and predict consumer behavior while also reducing associated risk of the patient population.

Fraud and Abuse: SAS Fraud Framework can dramatically help with fraud and abuse by identifying fraudulent spending and upcoding among a payer’s population.

Failure of Care Delivery: More and better health care analytics can help in two ways. First, analytics can help improve outcomes by speeding the R&D process to get better care and treatments to patients faster. Second, analytics at the point of care can help practitioners better determine the most effective clinical option.

Failures of Care Coordination: Poor communication among practitioners providing care to a patient can cause higher readmission rates, missed appointments, poor medication adherence and thus worse clinical outcomes and higher cost. SAS Episode Analytics can help identify these failures for practitioners to fix.

Overtreatment: Again, SAS Episode Analytics can help to identify areas of consistent overtreatment and thus avoid it moving forward.

Administrative Complexity: This area of waste is not as addressable by analytics due to a variety of political and bureaucratic issues. However, a variety of analytical tools can help payers and providers identify certain inefficiencies in the ecosystem and thus provide rationale for fixing them.

All of these issues are certainly complex and there is no easy or straightforward fix. Yet using analytics to begin to identify the precise waste within each one of these categories is a great start.

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How SAS can improve the IoT experience

The Internet of Things, event stream processing and wearable devices such as Apple Watch and FitBit, just to name a few, all have massive potential to meaningfully contribute to the broader health care world. They accomplish this by transmitting personal health data in near-real time in support of potential diagnosis and treatments scenarios aligned with what we understand as eHealth, but there are many challenges.

Here are three of many examples that help you understand the practical implications of this trend. In the first, we are introduced to Apple HealthKit:

"With HealthKit, developers can make their apps even more useful by allowing them to access your health data, too. And you choose what you want shared. For example, you can allow the data from your blood pressure app to be automatically shared with your doctor. Or allow your nutrition app to tell your fitness apps how many calories you consume each day. When your health and fitness apps work together, they become more powerful. And you might, too."


Under Armour takes a slightly different perspective, they are embedding their clothing and fitness gear with sensors to create “the ultimate performance and health app.”

And, finally, a real-world example of the potential of all of this technology to save lives:

“Paul Houle, a 17-year old who plays nose tackle for the Tabor Academy football team, credits his brand new Apple Watch for saving his life. Houle had just wrapped up a practice session when he started feeling pain in his chest and back. Using his Apple Watch, he discovered that he had a heart rate of 145 for two hours after practice ended. After calling his trainer, Houle's heart rate was checked manually, resulting in an immediate visit to the hospital.

Houle was diagnosed with rhabdomyolysis, a condition that results when heavy exercise leads muscle cells to leak enzymes and proteins. Rhabdo can lead to kidney failure and death.”


We are experiencing explosive growth of individuals using commercially available personal health monitoring devices, which are able to exchange data with a variety of connected software and hardware platforms. As those data become available for analysis, as part of an ever changing and personal “digital health picture”, it is inevitable that information will make their way into more formal data streams that clinicians use to diagnose and treat ailments.

This situation presents a significant amount of risk to individuals and institutions that are interested in using these devices and data to make health care decisions at the personal and population levels. More importantly, the challenge of embedding into these devices a level of intelligence so that clinically risky or dangerous readings will appropriately alert the individual and/or the clinician, without that alert being ignored (alert fatigue), is a profound and untested challenge.

As the Internet of Things becomes omnipresent and the concept of eHealth an everyday reality, the problem of alert fatigue will grow geometrically and the distinction between data collected, transmitted and processed from clinically validated systems to personal, consumer devices will be difficult to distinguish.

Today, consumers and clinicians are at risk for not noticing or ignoring alarms and alerts generated by IoT connected devices that may lead to injury or death. For SAS, this represents an opportunity to develop embedded, in-line analytic processes that facilitate the use of streaming data quality functionality as well as advanced analytics to develop intelligent, proactive and learning alert systems. Paraphrasing a well-known marketing slogan, “SAS may not make IoT devices but, we make the data they generate better”. And SAS is beginning to do just that.

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Maximize global revenue from pharmaceutical product launches

The high cost of pricing myopia

When launching a pharmaceutical product, it’s not enough to negotiate the best reimbursable price for each local market. Overall financial performance will depend on successfully managing go-to-market strategies at the global level. That means determining the optimal price and launch sequence for all the countries in which you intend to commercialize a new product.

It’s a complex equation. External market forces, governmental pricing pressures, parallel trade and global reference price modifications have not only reduced launch revenue but caused in-market selling prices to erode by 3-6 percent a year. Even a slight price change in one country can mean a big hit to prices, revenues and margins around the globe.

A key influence in this trend is international reference pricing (IRP), also known as external reference pricing (ERP), a cost-control approach that is widespread. It’s the practice of regulators using the pricing of a medicinal product in one or more countries (a reference basket) to derive a benchmark for setting or negotiating the reimbursable price of the same (or similar) product in that country. For example, a commonly used rule stipulates that when a product is launched in a given country, its price must be no higher than the average price for that country’s reference basket.

Why it matters

Traditionally, products were often launched as soon as regulatory approval was received – or larger markets were launched first. Now that pricing across international borders is based on a complex web of interdependencies, it’s no longer enough to use broad-brush rules of thumb and a local focus. The challenge for pharmaceutical companies is to negotiate the best possible launch prices across all countries and carefully time the launch to minimize the influence of reference pricing as long as possible – all while balancing global revenue targets.


The benefits of optimizing the launch sequence strategy are self-evident, but there are challenges to the ideal.

Complexity of the question

The reference pricing matrix around the globe is daunting and ever changing. Most countries use their own formulas, and the rules that govern IRP are complex. Several of the largest, most influential and most referenced countries are evolving their reimbursement and referencing rules. Even a slight price change in a referenced country can have a significant impact on prices, revenues and margins around the globe. Navigating this matrix to limit price erosion becomes a serious optimization exercise.

For example, a product launch across 75 countries in 60 months represents trillions x trillions x trillions of possible price/launch date sequence combinations. Not all of those combinations will be viable, but you still get the idea. Finding the optimal combination is an enormous needle-in-the-haystack calculation.

Lack of global vision and governance

Organizational capabilities for price optimization are often cobbled together and do not adequately support today’s business requirements. Few pricing teams have a corporate mandate to manage launch strategies across geographies. As a result, pricing and launch sequencing decisions are often made at the country level without awareness of the big-picture ramifications. Companies that don’t have a centralized framework for managing launch strategies tend to make reactive pricing decisions and forego tens to hundreds of millions in revenue.

Absence of advanced analytics

According to an Accenture study on the pharmaceutical industry, 80 percent of executives surveyed said that pricing optimization is one of the top three strategic priorities for their companies, two-thirds of pharmaceutical companies do not have sophisticated pricing capabilities. In fact, about 70 percent of the industry is still using spreadsheets to figure out this problem. A few are using basic analytics in homegrown systems.

Slow time to results

Global pricing teams are tasked to run many analyses to simulate different market pricing situations. One client reported that each scenario required 12 hours of computer time to run, only to restart the next day with new assumptions. This iterative approach slows the pace of decision making, limits the number of scenarios that can be considered, consumes valuable resources and leads to organizational bottlenecks.

What is needed

Some life sciences companies have taken steps to formalize the launch process, but most still need to gain the abilities to:

  • Quickly simulate new product launches to see the effect of different pricing and sequence strategies.
  • Optimize the launch price and country launch sequence, including launch date as a variable.
  • Monitor in-market prices while considering the impact of mandated price changes and market events.
  • Centralize business structure and processes around global pricing

Launch revenue optimization is one of the most complex areas of analytics to solve, but the returns for the business are significant. Join our experts Patrick Homer and Ivan Oliveira at Analytics 2015 to discover more.

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The top 10 use cases for analytics in high-growth health technologies

Healthcare IT News recently published an article on 18 health technologies poised for big growth, a list culled from a HIMSS database. The database is used to track an extensive list of technology products that have seen growth of 4-10 percent since 2010, but have not yet reached a 70 percent penetration rate.

It strikes me that advanced analytics could have a significant impact on the greater adoption of these technologies and to further their capabilities and deliver increased value.

Because some of health technologies are already based on analytics – forecasting or optimization (e.g. cost/utilization analytics) – I didn’t include them here. This post instead takes a look at 10 use cases that can benefit from the greater use of analytics:

  1. Bed management – Remaining first-time buyers: 49.7 percent

Bed management technologies provide visibility into how beds are used and when they’re in use. What if you applied forecasting to this data so that you could see peaks and troughs of use across the hospital and network and the forecasted demand for specific skill sets to staff those beds? This information could improve planning for capital purchases of new beds, allow for preventive maintenance scheduling on beds and improve staffing across hospital and network operations.

  1. Business intelligence – Remaining first-time buyers: 40.3 percent

Traditional business intelligence is a historical assessment to determine what happened and why it happened. Forecasting analytics and scenario analysis can provide a forward-looking view to what is likely to happen based on various courses of action. Optimization analytics provides the insight to deliver the ideal outcome based upon a specific course of action. If you’re not using forecasting, scenario analysis and optimization, you are missing a significant opportunity to improve operations and clinical care. Forecasting enables you to understand what is most likely to occur and allows you to assess various scenarios to choose the optimal course of action.

  1. Data warehouse – Remaining first-time buyers: 39.7 percent

Modern data warehousing can be distilled to one word – Hadoop. And this is not a what-if option (as with the other technologies). This is a you-must-embrace-it-now solution. It’s a data store environment that is low-cost, flexible, scalable, fault tolerant and can process analytic models using huge volumes of data within the data store.

  1. Dictation with speech recognition – Remaining first-time buyers: 44.4 percent

You can now use text analytics to move natural language processing closer to unstructured text and also use it to update patient records. Neuro-linguistics and medical ontology can be used look for inaccuracies or contra-indicated treatment activity so that they could be identified sooner rather than later. Ultimately, this could mean identifying the appropriate patient record to automatically append the new dictation or halt an inappropriate procedure or treatment.

  1. Enterprise master patient index (EMPI) – Remaining first-time buyers: 39.6 percent

EMPI can use existing data management tools (instead of a purpose-built application with limited utility) to create a master patient record. The benefit of this approach is that the data management software has more utility across the enterprise. Additionally, robust data management tools allows you to make better use unstructured data to enhance match rates and verify (or improve) data accuracy for match decisioning. This results in easier record review and reconciliation.

  1. Enterprise resource planning (ERP) – Remaining first-time buyers: 65 percent

ERP enables inventory optimization. By optimizing your inventory, you can keep just enough supplies to help ensure there are no stock outs that could affect the quality of care or operations. It also means you minimize carrying costs, required shelf space and real estate and can help you avoid holding items beyond their shelf life.

  1. Infection surveillance system – Remaining first-time buyers: 49.3 percent

Infection surveillance systems use analyses to identify areas of operational improvement for preventing infection, monitoring infection control initiatives and assessing the impact of infection control programs. These systems can be further enhanced by using a Hadoop data store to access a significantly larger data set for correlation analyses, forecasting and prediction analysis. Imagine what it would mean for your organization if data such as patient longitudinal views, staffing levels, patient volumes, procedures, complications, event locations, time of day (and more) could be used for uncovering insights.

  1. Laboratory (outreach services) – Remaining first-time buyers: 41.8 percent

Imagine if you could use analytics to stratify patients based on their likelihood to adhere to treatment and then have laboratory outreach services focus their attention on those patients likely to benefit the most (e.g., those most likely to be non-adherent). Outreach services could orchestrate patient compliance programs and use resource allocation models to determine support resources needed at the individual level.

  1. Medical necessity checking – Remaining first-time buyers: 32 percent

Medical necessity checking has been referred to as "next-level clinical decision support." This ability is largely rules-based. What if you could use advanced analytics (in real time) to assess risk and receive recommendations when additional tests are warranted; or, that a patient is at low risk and testing is not warranted? This concept also applies to prescription treatments and other procedures. It provides another source of information and context for clinical recommendations.

  1. Staff scheduling – Remaining first-time buyers: 42.2 percent

It is interesting to note that this technology has less than 60 percent penetration in the market. The reason could that many smaller hospitals do not see a need for it. Or, perhaps the benefits of collecting the data are unclear because of limits of their existing technology. But what if, in addition to the traditional forecasting staffing demands, they also used analytics to assess staffing levels based on patient outcomes? Often, staffing levels are based upon patient volume, not patient outcomes. But what if you had access to recommendations (and the associated cost forecasts) comparing the use of full-time vs. part-time employees to cover demand peaks, weekends and holidays? This enables you to build the case for different staffing models in the future so that there is time to act rather than react.

Analytics can further increase the value of each of the above health care technologies by using the underlying data to provide insights and forecasts about current operations and the advantages and disadvantages of alternate courses of action. This analytical insight helps enable an organization to be proactive and realize additional savings and better patient outcomes.

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    Welcome to the SAS Health and Life Sciences blog. We explore how the health care ecosystem – providers, payers, pharmaceutical firms, regulators and consumers – can collaboratively use information and analytics to transform health quality, cost and outcomes.
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