Artificial Intelligence: 5 Realities for Financial Leaders

Artificial Intelligence: 5 Realities for Financial Leaders
The first step in a healthcare organization’s artificial intelligence strategy should be education.

Article, by Pam Arlotto, in the February 2020 issue of hfm magazine.

For PDF version: Artificial intelligence: 5 realities for financial leaders

Lyft, Uber expand reach into healthcare

Pam Arlotto was quoted in Modern Healthcare’s recent article, Lyft, Uber expand reach into healthcare.

Read the full article here.

UPMC, Carnegie Mellon to use Amazon’s AI Tools in Research

Modern Healthcare quoted Pam Arlotto in their recent article, UPMC, Carnegie Mellon to use Amazon’s AI Tools in Research.

Read the full article here.

Shifting from Volume to Value in our Healthcare System

Pam Arlotto was quoted in Healthcare Innovation’s recent article, That “One Foot in the Boat Problem” is going to Last for a While.

Read the full article here.

Consolidation Meets Disruptive Change

Pam Arlotto was quoted in Healthcare Innovation’s recent article, Consolidation Meets Disruptive Change.

Read the full article here.

Digital health companies see new ‘exit’ strategy with IPOs

Pam Arlotto was quoted in Modern Healthcare’s recent article, Digital health companies see new ‘exit’ strategy with IPOs.

Read the full article here.

Research on Hospital Patient Portal Use

Dr. S. Luke Webster, a consultant with Maestro Strategies, was quoted in Modern Healthcare’s recent article, “Black, older patients less likely to use hospital patient portals.”

Read the full article here.

Healthcare Analytics: Test the Waters Before You Dive In

Analytics as a Differentiator

Many healthcare organizations see the use of analytics as a primary differentiator in the journey to value-based care. The current analytics vendor marketplace includes:

  • Niche and point analytics systems (solutions focused on one functional area – marketing, finance, clinical, claims, quality measurement, human resources, etc.)
  • Enterprise analytics tools that are part of larger offerings such as ERP and EHR
  • Enterprise data repositories, data lakes and tools to support data normalization, aggregation, visualization, etc.
  • Extended enterprise systems such as care management platforms to support population health management and consumer activation
  • External industry comparative data sets

Most health systems use these systems for a variety of analyses including:

  • Descriptive analytics – to answer questions such as what happened; how many, how often, where, who…; what exactly is the problem; how did we perform, etc.?
  • Diagnostic analytics – to answer why is this happening and what are the trends?
  • Predictive analytics – to answer what will happen?
  • Prescriptive analytics – to answer how do we make it happen?

Advanced organizations are moving into Continuous Intelligence (CI). Rather than depending on traditional score cards, which require people to orchestrate every step in the analysis, CI uses artificial intelligence and machine-based algorithms to automatically interpret and harmonize the data. CI continuously discovers patterns and learns what’s of value in the data, and immediately sends insights to decision makers. In healthcare, this will be extremely valuable at the point of care, for care managers working across the continuum and for strategists who need agility in their decision-making processes.

Demonstration Projects Help “Test the Waters”

Yet, healthcare leaders often struggle with basic analytics projects. In fact, Gartner has uncovered an 85% failure rate on data projects. Ironically, this may be in part the result of executives trusting their gut rather than the insights derived from the data. Other problems include political, data quality challenges, limited data interpretation skillsets, resistance to change, governance, etc. It is often helpful, prior to jumping in the deep end of the analytics pool for the organization to first:

  • Learn new ways to think about data
  • Collaborate on new ways to use data
  • Leverage existing analytics tools and resources prior to making significant analytics investments
  • Develop consistent methods for defining the problem to be solved, defining the data, cleaning up data inconsistencies and improve on data presentation skills

In other words, “test the waters” prior to diving in. A Demonstration Project provides the ability to learn about insight-driven decision making through the execution of a clearly defined project. The Demonstration Project works best with a limited scope, limited resource commitment and within a short time frame. An Analytics Demonstration Project focuses on data quality, building trust and deploying a consistent data management methodology. Insights can be channeled into performance improvement projects, strategic and operational plans or other pertinent initiatives.

Leading Demonstration Projects

Often a “See One”, “Do One”, “Lead One” approach works well — an outside analytics advisor partners with the organization and leads the first project, the advisor partners with the organization on the second project and in the third project, the organization leads the project with the advisor serving as a mentor. Preparation and starting the project well are essential elements for success and key steps include:

  • Understand the problems you are trying to solve: Many organizations acquire solutions before they have identified what the organizational “points of pain” are. This results in multiple tools or platforms competing for leadership attention, budget dollars and resources. Identifying the problems you are trying to address, or the questions you are trying to answer will help prioritize the use of limited analytics capacity.
  • Evaluate your existing investments in analytics tools, systems and methodologies: It is essential to understand your current analytic tools footprint. Rather than jumping to acquire a new analytics system or service, you may find you are able to leverage existing investments. Over time, the lessons learned from successful Demonstration Projects will identify opportunities to improve your overall Analytics Strategy. Specifics may include rationalization of  duplicate systems and addition of new capabilities to close gaps based on your existing vendor’s development road map.
  • Break Data Analytics projects into smaller waves versus a big bang deployment: Focused sprints, using an agile methodology rather than a traditional IT waterfall project implementation plan, shifts the focus to performance improvement. For the purpose of planning a Demonstration Project there are many ways to break the problem into smaller, targeted efforts. So for example, a Sepsis prevention and reduction effort can be divided into multiple smaller Demonstration Projects including descriptive and diagnostic analysis of sepsis rates, use of data driven screening tools or protocols, sepsis prediction tools, stakeholder education through data, etc.
  • Ensure data integrity: Source systems (such as your EMR or niche systems) have varying quality of data. Oftentimes data are missing or stored in different places depending on clinical documentation practices. In addition, definitions of data may vary from system to system or even from stakeholder to stakeholder. A common “Data Dictionary” becomes essential. Demonstration Projects can be useful defining organizational steps in developing the data dictionary, mining data, validating data, creating data baselines, analyzing data, presenting and visualizing data, and developing a go-forward data improvement plan.
  • Communicate benefits to the organization, including executives: Use techniques to model value and even Return on Investment/Value of Investment. By showing how Data Analytics initiatives impact strategic goals as well as daily operations, the organization can begin take a holistic view and integrate insight-driven decision making at all levels of the enterprise.

Organizations who leverage the strategic value of data to make informed decisions will be the ones who not only survive, but thrive.

 

Data – The Star of the Show

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.

 

Beyond Return on Investment: Expanding the Value of Healthcare Information Technology, 2nd Edition

 

by Pam Arlotto

Contributors: Susan Irby

Beyond Return on Investment: Expanding the Value of Healthcare Information Technology, 2nd Edition this updated and revised edition provides lessons learned from healthcare IT adoption and the opportunity to drive value realization. From providing a basic primer on ‘how-to’ complete a Return on Investment analysis for a single project to developing a comprehensive program of value management to support the transition to high value healthcare, this book addresses emerging trends, practical approaches and measurement methods to help drive value.

Beyond Return on Investment, 2nd Edition views IT as a strategic asset in the transformation of healthcare. Based on previous editions, this book updates and identifies the components of an integrated value management strategy including value driven decision-making culture, an integrated approach to strategy development, a value based governance model, a process that defines business case development through ROI analysis, value measurement and value scorecard development. New chapters in this addition include a Framework for Value Management, validating vendor defined value and ROI, and new methods for realizing value.

Available through CRC Press, order here.