Pam Arlotto

Agile Decision Making: Combining Insights with Instinct to Navigate the Unknown

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Decisions are being made today that may alter a healthcare organization’s trajectory for years to come. The pandemic has revealed how unprepared we are as an industry to use real-time and predictive data to tell us “what is happening”, “what will happen” and “how do we make it happen.”

COVID-19 has upended conventional leadership thinking and challenged even the most astute to admit “we don’t know what we don’t know.”

 

We Must Think Differently

In recent years, the healthcare industry has made a significant investment in advanced analytics and encouraged a transition to insight driven decision making. A survey of healthcare executives conducted by Black Book in January 2020, indicated that the vast majority (93%) feel data analytics is crucial to helping meet future healthcare demands and make strategic decisions. Yet, utilization of advanced analytics was described as “negligible” by 80% of the respondents. 71% of surveyed executives said they were too busy to learn such systems. In our conversations with healthcare leaders, they indicate they prefer to delegate analysis to others, aren’t really aware of what data analytics options they have, and often prefer to rely on their instinct – after all, it’s what made them successful.

For many, they have relied on the hundreds of static spreadsheets and score cards providing weekly, daily, monthly, and quarterly reports on “what happened” to validate these instinctual decisions. Yet, in today’s world much of this history is not pertinent. Comparative benchmarks, based on pre-COVID operating models, are no longer valid. Even the most sophisticated data scientists cannot take this historic data, identify new patterns and build a model to predict the future based on our current circumstances in a rapid fashion. Even the fastest computer processor can’t generate the algorithms we need when the situation changes as rapidly as it does today. Advanced analytics tools such as machine learning and predictive modeling are at their best when the future environment is similar to the historic environment – and that’s not the case today!

Healthcare decision makers must think differently about the role of data in their decisions and their approach to making decisions.

Agile Analytics, Combining Insights & Instinct for Decision Making

In the emerging COVID world, decision makers need practical, short term, results driven answers to their questions. Influenced by interrelated factors such as numbers of positive cases, bed availability, medication and PPE supply, treatment protocols, deployment of virtual tools and the impact of social determinants on population segments, the problems we are trying to solve change constantly.

Agile Analytics is a new paradigm for healthcare decision makers and is focused on rapidly finding value in data.

COVID brings the need for quick turnaround of information and rapid decisions. Historic data trends and traditional, large scale analytics projects are not dynamic enough nor nimble enough to support today’s decision-making environment. Since “we don’t know what we don’t know”, we must learn along the way. Jumping to solutions, based on traditional best practice or the play books of the past may lead us down the wrong path. Yet, complex architectures and advanced analytics platforms may not be responsive enough to meet the challenges of the current environment.

Agile Analytics is a style of working and problem solving. Originally created by software developers in 2001, agile is a collaborative approach built on short one to three-week iterations resulting in a data output that can be used to solve a problem or make a decision. Each targeted iteration starts simply, explores key components of the problem and initial hypothesis, and provides decision makers directional insights to drive data stories and resulting conversations.

The Minimum Viable Analysis

A Minimum Viable Analysis (MVA), is similar to the Minimum Viable Product concept developed by Eric Reis of Lean Start-Up fame. “Rather than spending months even years perfecting a product (analysis) without ever showing it to a customer (decision maker)…we start with a simple prototype of the product (analysis) get feedback from the customer (decision maker) and learn, then build it out further through multiple feedback sessions and iterations.”

The MVA starts simply. As in the case of the minimum viable product, an initial problem to be solved or decision to make is identified by a small collaborative team of decision makers and data analysts. Using a travel analogy, rather than building an entire automobile to solve a transportation problem, the team starts with a skate-board as an initial prototype. This prototype defines data output, visualization or presentation expected from the MVA.

For problems the healthcare delivery organization is trying to solve during these times of uncertainty, a minimum viable analysis can be used to answer questions such as:

  • When will we run out of ICU beds/ventilators?
  • What staffing constraints will we have in acute care? in ambulatory? in post acute?
  • What is changes should we make in our care model given the mix of virtual and in-person visits?
  • What triggers will change our plans for rescheduling visits and procedures?
  • Will we have PPE limitations or other supply chain issues?

Then, as specific questions are answered, an iterative approach can be used to add to the knowledge base and analysis to produce answers to more targeted questions or broader concerns.

We term the gap between the data output from our agile analytics effort and the change in behavior required to solve the original problem as the “Last Mile.” Unfortunately, the Last Mile is often an afterthought in analytics projects. Frequently, leading edge analytics initiatives don’t generate value or results due to ignoring the last mile. Resistance, lack of trust and transparency, siloed based behaviors, and a variety of last mile issues can often be anticipated and planned for early as problems and hypotheses are defined. Decision makers have a unique understanding of the environment where the desired insight-driven changes should occur. Instinct driven decision making plays an important role in defining Last Mile issues, early in the decision process.

COVID-19 brings a great deal of uncertainty, and decision makers can’t delegate data analysis as they did in the past. Multiple perspectives, judgement and experience are needed to collaboratively define the problems to be solved, potential hypotheses, explore the implications of analyses findings and communicate insight implications.

While data driven insights can provide new “aha’s”, they are best when combined with the decision maker’s instinct to navigate these uncharted waters.

Pam Arlotto

Data – The Star of the Show

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One-week post #HIMSS19, blogs and articles are using phrases like “no one unifying theme”, “something for everyone”, “the invasion of non-healthcare high tech” and “the end of the EHR movement” to summarize the global conference key take-aways. Even though the exhibition showcased interoperability, artificial intelligence, telehealth, security, the internet of things, precision medicine and more, the focus was not on these technologies. In fact, a more subtle underlying reality was clear in both the education sessions and vendor booths. Data is the star of the show!

Data in the Spotlight at HIMSS

Data collection, data integrity and quality, data access, data for benchmarking and comparative analysis, data protection and safety, data as part of care management platforms, data analysis and prediction, data visualization and ultimately, the transformation of data into information each had the spotlight. Data and analytics vendors were certainly a major focus of the show. These vendors were not outdone by solutions which offered data a by-product of a much more comprehensive offering such as Enterprise Resource Planning or data embedded within cloud-based services designed to solve specific business and clinical problems such as readmissions. Moreover, CMS took center stage in multiple forums to discuss the Interoperability and Patient Access Proposed Rule. With a goal to touch all aspects of healthcare, from patients to providers to payers to researchers, “CMS hopes to break down existing barriers to important data exchange needed to empower patients by given them access to their health data,” CMS Administrator Seema Verma indicated. HIMSS even gotten directly into the act. Recognizing the importance of data and information, they have changed their vision statement from “better health through IT” to “better health through information and technology.”

Siloed Organizations and Turf Battles

Yet, even with all of this attention and applause its hard to reconcile the maturity of data initiatives in many health and healthcare organizations today and the challenges faced by many. For data to be valuable, data must be converted into information, information into insights, insights into decisions, and decisions into action. Unfortunately, many health and healthcare still manage their data assets within siloed organization structures. Turf battles are common. Data-related issues for decision makers often include:

  • Confusion over who “owns” data and analytics
  • Questions regarding centralization versus decentralization of analytics resources and tools
  • Limited trust in the data and reports created by other parts of the organization often resulting in considerable rework
  • Inconsistent data definitions, duplicate data sources and systems, and costly resource requirements
  • Analysts who don’t understand the problem to be solved when data requests are “thrown over the fence”
  • Proliferation of spreadsheets, manual manipulation of score cards, and limited automation
  • Delays and slow turnaround of data requests
  • Data repositories that create many but rarely used reports
  • An inability to fully appreciate much less realize the benefits of big data, and predictive and prescriptive analysis

According to thought leaders at SAP, “less than 1% of the world’s data in business is analyzed and turned into benefits”.

The Enterprise Analytics Management System

Creation of an organizational approach to standardizing management of data, or Enterprise Analytics Management System (EAMS), results in a defined, documented and deliberately managed set of priorities, polices, procedures and processes. The EAMS should address the collection, definition, analysis, interpretation, translation and presentation of data to a wide variety of audiences.

Objectives for the EAMS are to:

  • Transition from a data and analytics departmental/siloed approach to a clear, consistent enterprise approach to managing data assets
  • Ensure key stakeholders understand enterprise analytics assets and have a consistent methodology for working together across the organization
  • Clearly define an enterprise analytics organization and operating model
  • Build a culture of collaboration and accountability to support data and analytics
  • Design and implement an oversight process or governance process
  • Develop an Enterprise Analytics Strategy and Pragmatic Road Map for the next few years

For data to truly be the star of the show, we must improve our ability to govern and manage this critically important asset.

 

Pam Arlotto

Disruptive Innovation v. EHR Optimization: Is the Tail Wagging the Dog?

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Disruptive innovation in healthcare will depend on new combinations of data, technology and business models to create new interactions with health and healthcare consumers. In a NEJM Catalyst Marketplace Survey, healthcare executives, clinical leaders, and clinicians ranked the healthcare sectors in most need of disruption. The top three sectors were hospitals and health systems (65%), healthcare IT vendors (47%), and primary care (36%). Interestingly, a dichotomy emerged when respondents considered whether buyers were willing to pay for solutions to result in disruptive innovation. Most notably, health care IT shot to the top of the list, named by half of respondents. Hospitals and health systems were second (46%).

Importance of EHR Optimization

Yet according to a recent Health Data Management survey, 72% of respondents from healthcare organizations indicate that achieving EHR optimization is either extremely important or very important for their organizations. Healthcare leaders vary in their definition of optimization. For some it consists of routine maintenance, for others it involves remediation of technical issues not addressed during implementation, and for others it includes the addition of new functionality. The performance-improvement minded define optimization as including standardization of workflows, improved use of data and application of best practices. There are three problems with this thinking:

  • At best optimization produces incremental performance improvement and change, resulting in a nominal return on investment and value,
  • Second, optimization is focused on the technology not the healthcare business or clinical problem to be solved – resulting in the proverbial technology tail wagging the dog, and finally
  • There is a perception that disruptive innovations must come from outside the industry, and if the data and technology leaders are “heads-down” focused on optimization, they may miss the chance to drive real change

Solving Narrow Business and Clinical Problems is the Key to Disruptive Innovation

Disruptive innovation requires one to solve the business and clinical problems of the industry. These problems are big, complex and often beyond the control of individual practitioners and health systems. For the best lesson on addressing complex problems, we can look within our own industry. Cancer, once a death sentence, was the focus of doctors and researchers for years. The common thinking was that a single cure for all forms of the disease would be the answer. Physician Sid Mukherjee, author of book The Emperor of All Maladies, describes the first breakthrough.  Sidney Farber, now known as the Father of Modern Chemotherapy, decided to focus exclusively on treating leukemia. By narrowing his focus Farber was able to make remarkable progress against this single condition. As a result, his work led to new protocols and treatments for other cancers. According to Mukherjee, “focusing microscopically on a single disease, one could extrapolate into the entire universe of diseases.” The healthcare industry can learn and apply this lesson – to solve solve large complex problems, first attack smaller micro-problems.

Move to the Next Level of Value & Return on Investment

There exists a full spectrum of high-impact value that can be realized and created when investments in HIT and digital tools are applied to solving healthcare business and clinical problems. The Healthcare Value Pathway illustrates the next levels of value and return on investment.

Key steps include:

  • First of all, start with a narrow focus on a specific problem such as the historic under-investment in primary care, the cost of a hospital stay, patients with multiple chronic conditions, the disparities in access or challenges in transitions of care
  • Next explore specific innovations such as:
    • Design new business and care delivery models
    • Develop new networks new networks of patients and providers
    • Create new approaches to sharing information
    • Reinvent work processes, decision making structures and roles/responsibilities
  • Analyze market, clinical, financial, claims, social determinant, etc. data to learn more about the problem to be solved
  • Finally, iterate micro-phases of designing and piloting the innovation

Oh, and what about technology? Technology is and will be pervasive in all that we do in health and healthcare. Consider as you design new innovations, potential high-impact or value-added technologies.  Rather than “wagging the technology tail”, move beyond optimization to focus value through disruptive innovation.

Pam Arlotto

The Pivot: From Compliance to Strategy

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HIMSS16 – billed as the largest and most important healthcare IT conference in the United States occurred last week in Las Vegas.  The message was loud and clear – something is different; the government mandate is over.  Strategy is the new, new.

For years the HIT world has encouraged alignment of enterprise strategy and the IT plan.  Alignment suggests two distinctly different things creating a linkage or connection.  Healthcare enterprise strategy decisions such as which markets do we enter, who do we acquire, which service lines do we emphasize, and what capital investments do we make are explored at executive and board levels.  Operations and financial decisions to support our hospitals and physician practices are made within organizational silos.  Sometimes IT is at the table, but more often than not information systems professionals are called in after the fact to “implement” selected systems and tools.  Sophisticated IT organizations have created IT Strategic Plans, IT Governance structures, IT Road Maps, and IT Champions/Customer Relationship Managers.  Our challenge – separate, sometimes aligned but rarely one.

Uncertainty is the new normal.  Strategies that take years to implement, vendor partners who are all vying for the same space and the challenges of mergers and acquisitions are driving us from 1.0 healthcare – where business as usual no longer is sustainable.  We are at a cross roads.  Those of us in transition must “pivot” our viewpoint from 1.0 volume based thinking to 2.0 and beyond.

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We need fresh, new perspectives regarding the relationship between enterprise direction and the digital strategies required for the future.  New harmonized strategies will:

  • Vary by geographic market and depend on community progress toward clinical integration
  • Necessitate partnerships, alliances and consolidations – no one can fund the investment alone and no one vendor will have all the solutions
  • Require governance models that address horizontal, vertical and virtual decisions making and integrate change across multiple systems of care
  • Move from an applications focus which emphasizes feature, functionality to a platform focus, producing highly configurable systems which will drive standardization and enable business strategies simultaneously
  • Redesign our organization structures, leadership competencies and operating models in IT, Informatics, Analytics and Quality
  • Acknowledge our work to create systems of documentation was foundational but not the end goal; systems of insight and behavioral change are the next stages in the evolution
  • Result in convergence of people, process, information, change and technology to rationalize costs, manage risks, realize value and activate patients to become involved in their care

 

Pam Arlotto

Beware Best Practices

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Almost twenty years ago, in 1996 after publishing “America’s Health in Transition: Protecting and Improving Quality”  the Institute of Medicine launched a long term, ongoing concerted effort on assessing and improving the quality of healthcare.  “To Err is Human” further galvanized the national movement to improve the quality and safety of our healthcare practices by putting the spotlight on how tens of thousands of Americans die each year from medical errors.   The “Quality Chasm” report underscored the importance of a dramatically improved information technology infrastructure to support a 21st century health system.  Building blocks for such a system include an electronic health record system and national standards.  Progress has been made, the federal government has paid out over 30 billion dollars in Meaningful Use incentives as of March 2015 and impressive examples of quality improvements are frequently quoted in the literature.  Yet, most would agree that the results to-date have been underwhelming.

It is important to recognize that most implemented EHRs with a “check-the-box” mentality order to comply with Meaningful Use.   When Meaningful Use was initially launched, our team suggested that we were “enabling the dinosaur”.  And while not prehistoric, the design of today’s healthcare system does have ancient roots.    The Romans constructed buildings called valetudinaria for the care of sick slaves, gladiators, and soldiers around 100 B.C. (Heinz E Müller-Dietz, Historia Hospitalium, 1975).  In the U.S., the number of hospitals reached 4400 in 1910, when they provided 420,000 beds (U.S. Bureau of the Census, Historical Statistics of the United States 1976).  So clinical information technology was about automating existing clinical processes in hospitals (Stead 2005) rather than transforming clinical decision-making and work processes across the care continuum” (Brown, Patrick, Pasupathy 2013). 

 Separately, quality and performance improvement departments focused on deploying best practice – a method or technique that has consistently shown results superior to those achieved with other means, and that is used as a benchmark. (Wikipedia).  While best practices have their place, it is important to recognize the risks associated with emulating others when the practice depends on an antiquated business model such as hospital care. JPGshutterstock_159756653

As health systems transition from 1.0 – Bricks and Mortar Healthcare to 3.0 – Digital, Value Driven Connected Health and Healthcare, we encourage a focus on emerging practice.  A concept born in “systems thinking”, emerging practice assumes:

  • We cannot copy other organizations, use it in our organization and expect it to work given the number of variables at play
  • Intentional design of care management and business models will result in disruption of today’s best practices
  • Collaboration and integration of clinical teams, business leaders, information technology experts and data analyst will create new value
  • Big bang, long term projects are giving way to agile, experimentation where we learn to work in new and different ways
  • Rather than using our intuition or past experience to drive improvement, data driven innovation can often have more remarkable results and new practice will emerge

So, the next time someone mentions “best practice” challenge their thinking.