What can Healthcare learn from Supply Chain Analytics?

Introduction

As an industry, healthcare is facing its share of disruptive forces. The mere threat is resulting in healthcare organizations (HCOs) pursuing innovation and transformation in healthcare delivery. A key tool to assist HCOs in the transformation journey is advanced analytics. Real transformation, cost savings, increased efficiencies and other improvements come when HCOs turn data into meaningful information for decision-making purposes. Without it, HCOs don’t know where to focus their efforts with precious limited resources. Supply Chain Management (SCM) analytics can be one source of powerful analytics to assist HCOs with comparative analyses and evidence-based decision-making. Examining the journey supply chains have pursued in order to respond to data and informational demands can be useful to understanding broader implications and usefulness of analytics across healthcare.

Supply Chain Management, while a critical function in all HCOs, is often overlooked as an opportunity area to progress analytics. Many view supply chains just as the physical products that flow from manufacturer to patients but there is another equally or more important perspective, the digital supply chain and the tremendous amount of information flowing in support of the care of patients. The “digital supply chain” is a virtual copy of its physical counterpart and for the most part how goods and services are purchased and delivered. It is also where significant untapped efficiency and value resides. This has proven out in other industries and strikingly apparent as you look at Gartner’s Top 25 industry supply chains (e.g. Amazon, Apple, P&G, Intel, etc.) (Aronow et al). These firms not only compete based on the capability of their supply chain but also their analytics capability and that is no accident. So why have HCOs not implemented or mastered the strategic advantage supply chain analytics can have on performance?

The answer may be dependent on whether or not the right question is being asked. The data and analytics are undeniably important, however, what is more important is how they align or conflict with individual and institutional behaviors (Schrage). An organization that has a mature and highly successful analytics program understands this and they embrace it as a strategic imperative and competitive advantage. Companies like Amazon, Google, Facebook and others are all great examples who compete on analytics. It is a differentiator for them and they have woven analytics into every decision.

So, is an advanced analytics capability a losing proposition in healthcare? Does it take an army of data scientists to make it work? No. Failure to develop a robust analytical strategy nearly always has the same root cause: there was never a realistic plan for success. While most will say they have a plan, most don’t. If a plan exists, has senior leadership endorsed and funded the plan? Can they describe what success looks like? In most cases, the answer is “No”. One reason for the lack of progress may go back to basic change management principles. To be successful with analytics requires a significant process change.

Analytics targets the very process that many leaders cherish and protect the most, “the decision-making process”. This is one reason why starting with the supply chain might be the answer. It is an area where change might be easier yet where significant direct value can be achieved. Additionally, it is an area that more closely resembles other industries and therefore requires less customization to make best practices work.

Ready, Set, Action

Analytics can be defined in many ways, but what matters is the purpose of analytics. Various levels of analytics maturity can be distinguished, depending on how much of the decision process is automated, and how much is left for human intervention. The Gartner diagram below (See Exhibit 1) shows the tradeoff between automatic analysis (the analytics part), and what’s left for individual intervention (the human part).

Exhibit 1: Four Types of Analytics Capability

The model importantly stresses that “action” is needed once a decision is derived for value to be realized. This is amplified in the supply chain and why analytics are so important to achieving an optimal performance level. It is well accepted that machines are more effective than humans in determining when and what to order, particularly when thousands of stock-keeping units (SKUs), hundreds of suppliers and hundreds of locations are to be considered. For this reason, the function of decision making and action is often relinquished to machines while humans instead provide oversight and monitoring.

From a broader perspective, this point is critical to understanding why many analytics programs are doomed from the start. This is also why “leadership” is the key to success as opposed to data and technology. (IBM Corp.) The truth is, most organizations can be successful with analytics but it will take more than buying popular business intelligence (BI) tools. Executive leadership and commitment to data-driven decision making and action is critical. Those that are seeking a strategic competitive advantage will need to look broadly at their organization as well as their ability to change some of their most critical decision-making processes. Minimally, HCOs need to begin their analytical journey. Progressing through different levels of analytical capabilities to further automation and achieve efficiencies and cost savings requires time. Leveraging your supply chain data and contributing to the understanding and management of a HCOs’ second-largest area of expenditures (behind labor) is of increasing importance in the current environment and a highly logical place to start. Supply chain data can be a significant source of information to assist HCOs in managing variation in pricing, utilization, outcomes, standardization, best supply practices, etc. As value-based purchasing or bundled pricing grows managing to a fixed reimbursement takes on added importance if not, financial survivability. Controlling total cost of care of which supplies represent a significant portion along with assessing the impact of new technology on revenue and reimbursement will be a requirement.

Analytics Maturity Models

Understanding where your HCO is functioning in terms of analytical maturity or the journey to be undertaken is a key first step. There are several maturity models that are available but all are similar in that they look beyond data and tools. The DELTA Model was developed in 2010 by Tom Davenport, Jeanne Harris and Bob Morison (see Exhibit 2). The model consists of five key elements that need to be assessed for any program to succeed.

Exhibit 2: DELTA Model Framework

With these five elements, Davenport, Harris and Morison go on to describe an analytics roadmap (See Exhibit 3) that outlines the five stages of maturity which are Impaired, Localized, Aspirational, Analytical and Competitor. Each stage describes typical behaviors, capabilities, and challenges that organizations typically experience.

Exhibit 3: Levels of Analytical Maturity

At the Impaired Stage, organizations lack the transactional data quality for analytics and decision- making. They should postpone plans for investments in analytics and instead fix their data. They also have a “data-allergic” management team that rarely uses data to drive decision-making. Top leadership generally believes analytics are synonymous with technology and aren’t learning from mistakes.

At the Localized Stage, organizations lack understanding as well as commitment. At this stage, analytics are localized and investments are generally limited to “prove-it” investments. This stage may be slow and circuitous. Success is achieved in small groups. However, each has fiefdoms that in the long run prove to be difficult to integrate.

The Aspirational Stage occurs when a top executive sponsors an enterprise analytics program. Executive sponsorship is so vital to analytics maturity that having the right sponsor may be enough to move an organization to Stage 3 without any other improvement. The organization is now able to clearly define its’ vision as well as expected benefits. Significant attention is paid to change management and a clear analytics hub is established.

The focus of Stage 4, Analytical, is executing the plan developed in Stage 3 to the point where significant capabilities are in place at the enterprise level. At this point the entire Executive Team is committed to not only the technology but also in leveraging analytics to drive performance. The hub is well established, the vision is clear as is the target.

Finally, at the Competitor Stage, analytics moves from being very important to the key enabler of its’ market differentiation. These organizations have fully automated analysis solutions that are embedded in their most critical processes.

Applying the Model

A successful analytics program must have leadership engagement, a realistic plan for success and the commitment to allow the plan to unfold. Applying a model like DELTA is a critical first step. A viable roadmap must be multifaceted and address far more than technology. It must address the typical behaviors, capabilities, and challenges that are to be overcome over the course of execution. Attempting this without a proven track record can be daunting. Why not start with supply chain? As stated earlier it has several factors that make it a prime candidate for quick success. Additionally, the supply chain cuts across nearly every department in a HCO so many staff are able to engage, experience, and learn firsthand from this area.

Furthermore, senior executives have a lot on their plates but without making it their responsibility their organizations will not properly invest resources, appreciate the outputs of analytics nor make the necessary changes to turn insights into action. Analytics will be relegated to a back-office function and analytics functions will continue to fall short of expectations. So why should busy executives reprioritize analytics? Due to technology, globalization, new disruptors and competition along with a general trend towards deregulation, competitive barriers are lower than ever. High-performance business processes are among the last points of differentiation. What’s left as a basis for competition is to execute your business with maximum efficiency and effectiveness, and make the smartest business decisions possible. This is why organizations should strive to reach Stage 5, Analytics Competitor.

Additionally, new data management factors have emerged for HCOs that can be summarized in three words; ownership, access and usage. These are very important concepts that have to be addressed from a policy standpoint, information technology connectivity, and in any partnership or contractual agreement. Make no mistake that ownership of data and subsequent information is highly valuable and can easily be turned into a revenue generation opportunity. Maintaining control over data ownership and how it is used not only has legal and regulatory risks associated with it but will be critical to a HCO’s competitive position. Don’t relinquish ownership of your data. Once secure, controlling how it is accessed and used is also very important. In this age of increasing cybersecurity risks, “who” accesses the data and for what purpose must be closely managed and secure from a process management and information technology standpoint. Data, your data, is a strategic asset.

Conclusion

Many have reached the destination of deploying a single electronic medical record (EMR) as well as an enterprise resource planning (ERP) system. But the journey is far from over. There is plenty of experience as well as literature questioning what if any value has been achieved in the implementation of these systems. The truth of the matter is that transactional systems, like the EMR and ERP, rarely deliver significant value and in some cases add to overall cost. However, HCOs are resilient organizations and committed to continuous improvement. Such efforts will lead to a new and next phase which will be heavily focused on optimization and efficiency. Analytics-driven action will be at the cornerstone and will be a catalyst for those that chose to compete. The problem for most is getting started with a willingness to rethink critical decision-making processes and remaining committed to action. Most HCOs would benefit by seriously contemplating the supply chain as a progressive and effective launchpad for success.

References

  • Gartner Inc. (May 2018). The Gartner Supply Chain Top 25 for 2018. Stan Aronow, Kimberly Ennis, Jim Romano. (Aronow et al)
  • Harvard Business Review (April 2014). Why Your Analytics are Failing You. Michael Schrage. Downloaded from https://hbr.org/2014/04/why-your-analytics-are-failing-you. (Schrage)
  • Harvard Business Review Press (September 2017). Competing on Analytics. Thomas Davenport, Jeanne Harris. (Davenport et al)
  • IBM Corp. Downloaded from https://www.ibm.com/developerworks/community/blogs/jfp/entry/the_analytics_maturity_model (IBM Corp.)
  • Picture (Exhibit 1): https://www.gartner.com/newsroom/id/2881218
  • Picture (Exhibit 2): https://iianalytics.com/research/building-an-analytics-team-for-your-organization-part-i.
  • Picture (Exhibit 3): https://iianalytics.com/analytics-resources/delta-model-five-stages-of-analytics-maturity-a-primer.