What is the message?
Implementing predictive analytics tools in healthcare holds immense potential for reducing waste and improving care outcomes.
However, successful adoption requires strategic engagement at all levels of the organization, from frontline staff to executive leadership.
This summary is based on the article “Getting Buy-In for Predictive Analytics in Health Care” published by Harvard Business Review and written by Meetali Kakad, MD, Ronen Rozenblum, David Westfall Bates, MD on June20, 2017.
EXECUTIVE SUMMARY
What are the key points?
Engage the Right People from the Outset
Successful implementation necessitates involvement of multidisciplinary teams with expertise in clinical, analytics, data science, IT, and behavior change.
Demonstrating the clinical impact of predictive tools, such as reducing unnecessary antibiotic use in newborns, can drive buy-in from frontline staff.
Change Agents and Clinical Champions Are Essential
Implementation experts and clinical champions play a pivotal role in driving behavior change and supporting workflow integration.
Examples like a public hospital in the Southern U.S. showcase the transformative impact of clinical champions in promoting predictive analytics adoption.
The C-Suite Must Commit
Executive leadership, particularly CEOs, must understand the benefits and actively support the integration of predictive tools.
Metrics such as reduced readmissions, which resonate with management due to potential financial implications, can underscore the value of predictive analytics.
What are the key examples?
Kaiser Permanente in Northern California reduced antibiotic use in newborns by 50% using predictive tools, without increasing sepsis-related complications.
A leading public hospital in the Southern U.S. established a center for predictive analytics through the efforts of a small group of physicians, leading to widespread adoption of predictive models.
What are the key statistics?
The U.S. healthcare system spends $750 billion annually on unnecessary services and inefficient care.
A predictive model aimed at reducing readmissions for heart failure patients demonstrated a 26% reduction likelihood of readmission.
Conclusion
Successful implementation of predictive analytics tools in healthcare hinges on comprehensive engagement strategies, encompassing multidisciplinary teams, clinical champions, and committed executive leadership.
By prioritizing communication, change management, and seamless integration into workflows, healthcare organizations can maximize the benefits of predictive analytics, ultimately leading to improved care outcomes and cost savings.
DEEP DIVE
Getting Buy-In for Predictive Analytics in Health Care
Harvard Business Review
by Meetali Kakad, MD, Ronen Rozenblum, David Westfall Bates, MD
June 20, 2017
Table of Contents (TOC)
- Introduction
- Engage the Right People from the Outset
- Change Agents and Clinical Champions Are Essential
- The C-Suite Must Commit
According to the National Academy of Medicine (formerly the Institute of Medicine), the U.S. health care system spends almost a third of its resources — $750 billion annually — on unnecessary services and inefficient care.
New predictive analytics tools promise to reduce waste and improve care by forecasting the likelihood of an event — for example, a patient being readmitted to a hospital or developing a life-threatening infection — and allowing providers to tailor treatments and services accordingly.
These tools are now being used across the continuum of care, from disease surveillance to chronic disease prevention to identifying patients who are at risk of deterioration.
According to the National Academy of Medicine (formerly the Institute of Medicine), the U.S. health care system spends almost a third of its resources — $750 billion annually — on unnecessary services and inefficient care.
New predictive analytics tools promise to reduce waste and improve care by forecasting the likelihood of an event — for example, a patient being readmitted to a hospital or developing a life-threatening infection — and allowing providers to tailor treatments and services accordingly.
These tools are now being used across the continuum of care, from disease surveillance to chronic disease prevention to identifying patients who are at risk of deterioration.
But despite the tools’ power to improve care, most health care institutions are not yet using them.
Among the impediments to adoption are the bewildering array of options providers face,
- from mobile applications
- to web-based tools to programs that integrate with electronic health records.
To better understand what stands in the way of adoption, and what facilitates successful implementation, we interviewed 34 key figures from leading U.S. health systems, policy makers, and predictive analytics vendors.
Among our most important findings: Success depends less on the tool itself than on getting buy-in at all levels from the start.
But despite the tools’ power to improve care, most health care institutions are not yet using them.
Among the impediments to adoption are the bewildering array of options providers face, from mobile applications to web-based tools to programs that integrate with electronic health records.
Among our most important findings: Success depends less on the tool itself than on getting buy-in at all levels from the start.
Here are three lessons:
Engage the Right People from the Outset
Regardless of whether a provider is developing predictive analytics in-house, as many large academic medical centers have done, or purchasing tools off the shelf, managers should make sure they are involving the right people throughout the entire process.
Homegrown tools require special development expertise, and both these and commercial tools require validation, implementation, evaluation, and ongoing improvement.
It’s necessary to have a multidisciplinary team, with clinical, analytics, data science, information technology, and behavior change skill sets available from start to finish.
A common reasons these tools are underutilized is that frontline employees don’t fully understand their value.
Thus, successful programs start with a problem where predictive analytics can make a clear difference.
A common reasons these tools are underutilized is that frontline employees don’t fully understand their value.
Thus, successful programs start with a problem where predictive analytics can make a clear difference.
For example, 50% of newborns with untreated sepsis (blood infection) will die. Therefore, healthy babies are often given antibiotics presumptively — “just in case” — which can lead to complications and increased antibiotic resistance.
Clearly, it would be desirable to identify newborns at low risk for infection and spare them the presumptive antibiotics.
Kaiser Permanente in Northern California has done just this, using a predictive tool to reduce the use of antibiotics by half without an increase in sepsis-related complications.
Demonstrating the clinical impact of a predictive tool can go a long way toward engaging those who will use them.
This is particularly important for clinical staff who may otherwise be skeptical of “black box algorithms,” whose inner workings remain hidden from them.
Demonstrating the clinical impact of a predictive tool can go a long way toward engaging those who will use them.
This is particularly important for clinical staff who may otherwise be skeptical of “black box algorithms,” whose inner workings remain hidden from them.
Bringing clinical staff on board early allows team members to influence which predictive tools are implemented and how, and to see early results.
While this can be time-consuming, the benefits cannot be overstated. This applies to both commercial tools and those developed in-house. Commercial vendors, in fact, may have to work even harder with staff to develop trust in their products.
Change Agents and Clinical Champions Are Essential
Without a clear implementation plan and staff skilled in supporting behavior change, implementation of a predictive tool can stall.
We’ve found that health care organizations that regularly used implementation experts to support change and improve quality across a range of IT and other types of projects had a head start when implementing predictive analytics.
These individuals work alongside clinicians to map workflows and identify what might need to change when introducing a new process or tool.
They may have a clinical background or one in service redesign or quality improvement.
Clinical champions have often proved to be essential in successful predictive analytics implementation — and health IT implementation generally.
Any group of change agents should include a subset of well-respected clinicians or other thought leaders in the organization.
These individuals should actively reach out to promote the tool, demonstrating its use and educating people about its expected benefits.
At one leading public hospital in the Southern U.S., a small number of physicians helped promote the use of predictive models throughout the hospital.
Their work gave rise to a center for predictive analytics, and today the institution uses these tools in numerous ways, including to reduce readmissions and to identify patients at risk of sepsis or returning to the intensive care unit.
The C-Suite Must Commit
Just as important as frontline buy-in is engagement from the top, especially from the CEO. Organizational leaders are often unfamiliar with advanced analytics technology and applications.
Educating leadership about a tool’s expected benefits is critical in generating support.
One large U.S. academic medical center did this by including tool performance measures in the executive dashboard, making its benefits clear to top management.
A tool’s value may be quantified in terms of quality improvement, improved patient or clinician satisfaction, or efficiency gains.
One measure that is likely to resonate for management is reduced readmissions among Medicare patients, as hospitals may be financially penalized for readmissions.
Models aimed at reducing readmissions among high-risk patients are understandably popular; one model, for example, was shown to reduce the likelihood of readmission for heart failure patients by 26%.
Ongoing attention from senior management is vital for the long-term sustainability of predictive tools; the models decalibrate over time and require regular maintenance.
Successful organizations take a lifecycle approach to managing and maintaining these tools, which requires budgeting for long-term resource requirements, including investments in improving data quality and infrastructure, recalibration, and in-house data science and technology capability.
Where commercial tools are purchased, costs such as software licenses, consulting, or other vendor-related fees also need to be factored into long-term budgets.
Implementing predictive analytic tools in health care is a means to an end — where the end should represent an improvement in health or health care outcomes, including lower costs.
Fully realizing the benefits from a specific tool requires a structured and thoughtful approach, involving the right people, with the right skills sets, at the right time.
As we’ve shown, the key to successful implementation has little to do with the model itself. Success depends on the time, effort, and resources set aside for communication, change management, and making the tool a seamless part of user workflow.
Clear, committed leadership and a culture strongly supportive of change and learning are also critical factors.
Done well, the result can be an increase in high-value care — that is, targeting appropriate health care to those who need it.
Done well, the result can be an increase in high-value care — that is, targeting appropriate health care to those who need it.
Originally published at https://hbr.org on June 20, 2017.
