Healthcare Analytics Summit 2017 Kicks Off: Wednesday Recap
The first full day of the 2017 Healthcare Analytics Summit™ (HAS) conference was packed full of riveting keynote addresses from Thomas Davenport, Eric Topol, and Dale Sanders; the HAS 17 Edition of Hollywood Squares from Tom Burton; and two waves of breakout sessions. Here’s our recap of what we learned on Wednesday at HAS.
To the largest audience in the event’s four-year history, Dan Burton, Chief Executive Officer, Health Catalyst, opened the 2017 HAS conference by introducing this year’s theme—changing the digital trajectory of healthcare. He summarized the challenges we all face as we go about this task. These include the upcoming digital medicine revolution, the accompanying tsunami of new data and new data sources, machine learning and artificial intelligence, understanding costs and pricing, and the next wave of population health.
As with previous HAS events, it’s not just all work and no play, with strategy games, old TV gameshows, theater, and superheroes combining to deliver learning and insight in a whole new way.
Dan mentioned the previous night’s notable experiences: the Analytics Walkabout, the Products and Services Showcase, and the Care Management Show, all running simultaneously and keeping attendees busy throughout the evening.
Burton shared information from the HAS 17 app attendee survey about the conference audience. He said attendee answers revealed a pragmatic group, who were realistic—yet optimistic—about promise of AI. This bodes well for HAS 17 attendees seeking to survive and succeed in healthcare transformation, as they’ll need both data and optimism. With regard to the current challenges in healthcare and the transforming digital trajectory, Burton noted that those who have courage can succeed and be a part of the transformation.
He reviewed a few of the attendee statistics, gathered from a pre-Summit survey:
- 67% are healthcare providers.
- 36% are senior and VP level.
- Most believe that machine learning will be fully integrated into patient care within 6 to 10 years.
- The favorite superhero is Wonder Woman.
- The favorite superpower is teleportation.
And Dan continued the superhero theme, by encouraging the audience that though they may not feel like a superhero, inside each of us there is one.
Keynote: The Four Eras of Analytics
Thomas H. Davenport, Visionary, Consultant, Professor, Author of “Competing on Analytics” and Others
In keeping with the HAS tradition of presenting speaker perspectives from industries outside of healthcare (i.e., criminal justice reformer Anne Milgram at HAS 16 and baseball analytics legend Billy Beane at HAS 14), leading business analytics expert Tom Davenport delivered the HAS 17 opening keynote address. His talk, “The Four Eras of Analytics,” looked at four iterations of data analytics—from initial small, descriptive analytics to the most recent generation of cognitive analytics—and how healthcare is doing in analytics, relative to other industries.
Tom described the four eras of analytics as follows:
- Analytics 1.0: Davenport calls the first era “artisanal” analytics. It’s still dominant in healthcare, where descriptive, small, structured data remains the norm. Data analysts are “back office” roles that don’t interface directly with leaders.
- Analytics 2.0: Data begins to get more complex and unstructured. The gap starts to close between analyst and decision makers.
- Analytics 3.0: Analytics start to drive products and services. Big data integrates with small data.
- Analytics 4.0: The fourth era is cognitive analytics, in which analytics drives automated decisions and technology (i.e., diagnosis and treatment), which take the place of some human jobs. Humans who are nimble and willing to learn new capabilities can move to augmentation of technology.
Analytics 4.0 is a fast-paced, fluid era. Healthcare needs to move faster to advance from early analytic eras and succeed. The industry is still wrestling with EHRs, mastering small data, and using descriptive analytics heavily. To move forward, healthcare needs predictive, prescriptive, and autonomous analytics, and it needs to embrace big data.
He offered five steps for surviving automation.
Stepping in: humans master the details of the system and when it needs to be modified.
Stepping up: how much automation do we need and where?
Stepping aside: humans focus on areas they can do better than machines.
Step narrowly: humans focus on knowledge domains that are too narrow to be worth automating.
Stepping forward: humans build the next generation of automated systems.
Fortunately, there is a prescription for surviving this automation/augmentation, that recommends we continue to evolve data management and analytics capabilities. Davenport’s good news for healthcare is that it’s digital trajectory is steeper than other industries. This means that while healthcare still has a long way to go, it’s poised to catch up.
Keynote: The Future of High-Impact, Precision Medicine
Eric Topol, MD, Physician Leader, Author of “The Patient Will See You Now,” and Others
While the healthcare industry uses the term precision (also known as individualized or patient-centered) medicine, keynote speaker Eric Topol, MD, reminded the audience that healthcare is currently a long way from this ideal. He explained that, far from an up-close, personalized view, the U.S. healthcare industry looks at medicine from a great, generalized distance.
The current approach is poor at discriminating each person’s risk, as evidenced by the prevalence of false positive diagnoses, non-responders to medication, medical errors, and misdiagnosis. Still, the U.S. will spend $3.4 trillion on healthcare in U.S. in 2017. Considering the rate of shortfalls in the system, this is “not a bargain,” Dr. Topol said.
Despite today’s status, the healthcare industry is capable of real transformation to patient-centered medicine, given the ever increasingly availability of technology to enable it. Dr. Topol cites the “Three Ds”—digitization, democratization, and deep learning—that will truly individualize medicine.
- Digitization will include game-changing capabilities, such as genomics to create individual risk scores, sequencing in of DNA in cancer and pathogens, and wearable sensors to measure physiological functions (i.e., cardiogram, glucose monitoring, and blood pressure). As these technologies become available on smartphones, patients will have increasing, immediate access to their own health information, allowing them to make data-driven decisions on a daily basis.
- Democratization, enabled significantly by wearable technologies described above, empowers the patient in their own care. Physicians and health system leaders will no longer own the data, as patients gain access to their personal information and are involved in generating it.
- Deep Learning, such as facial recognition, stands to remove the plateau in healthcare analytics and keep moving data forward, even surpassing human capability in some areas.
In all this technology we need deep empathy and connection. As machines grow smarter, humans need to become more humane. Topol ended with this quote from noted American physician, Francis W. Peabody: “One of the essential qualities of the clinician is interest in humanity, for the secret of the care of the patient is in caring for the patient.”
Keynote: Changing the Digital Trajectory of Healthcare:
Dale Sanders, Executive Vice President, Product Development, Health Catalyst
Dale Sanders doesn’t agree with the current digital trajectory of healthcare, hence his call to change it. Sanders, whose professional background includes equal parts military and healthcare, cited commonalities between the two industries that underscore the need for change. Both profit from adversity—the military from conflict and healthcare from illness. But, where the military has changed over the past 20 years, healthcare has not, signaling the dire need for transformation. And America spends 18 percent of its GDP on healthcare and only 3.3 percent on its military. If we don’t change the incentives that profit illness over health, we are all going to suffer.
While the military has changed and adapted over the past few decades to become much more effective, healthcare hasn’t. Dale says it’s time to raise our digital quotient. We need quicker time to value from the moment a clinical event occurs to when action is taken.
Enter the new Health Catalyst® Data Operating System (DOS™) solution, a health information exchange (HIE), clinical data repository, and electronic data warehouse (EDW). The platform enables applications on top of the platform and a stronger, hybrid architecture. DOS is fully digitization to capture the breadth of the human health data ecosystem and allows a new digital trajectory by digitizing the patient and the process. It’s a platform that has the ability to rapidly integrate data from hundreds of disparate sources and accelerate, for example, the value of healthcare mergers, acquisitions, and partnerships. Gartner calls it Hybrid Transactional/Analytical Processing (HTAP).
Dale emphasized the need to focus on cost accounting and transparency as imperatives on our digital journey. We also need to keep an eye on the soft side of humanity by finding, telling, and facing the truth…sometimes a nebulous concept in healthcare.
According to Sanders, the heart of healthcare’s required transformation is digital. To the C-level executives in healthcare he warned, “Raise your digital IQ…or else.” Along with digital IQ is digital quotient, an essential equation of asset times usage times data skilled labor. Healthcare must raise its digital quotient to succeed.
Dale emphasized the need to focus on cost accounting and transparency as imperatives on our digital journey. We also need to keep an eye on the soft side of humanity by finding, telling, and facing the truth—sometimes a nebulous concept in healthcare.
Breakout Sessions: Wave One
Session 5: Using Predictive Analytics and Effective Tools to Drive Down Infections (Case Study)
Kristen Kelley, MPH, BS, Director, Infection Prevention
Reducing incidence of central line-associated bloodstream infections (CLABSIs) would significantly improve experience for patients (for whom CLABSIs are a hardship—and yet another reason for hospitalization). Reducing CLABSIs would also save the health system money by reducing cost ($3,700 to $36,000 per case and government penalties for high rates of these infections). According to Kristen Kelley, MPH, BS, Director, Infection Prevention, her organization used real-time analysis, predictive analytics, and machine learning to identify patients at high risk for CLABSI and intervene accordingly.
A small, initial improvement team collected historical, previously siloed CLABSI data to get it into one place. To truly understand the data, the team needed all CLABSI data in one analytical mode that was easy to access and visualize, then worked with a system to analyze trends in one place (a scoreboard). The team built a dashboard to analyze data in real time and applied machine learning to predict infections in currently hospitalized patients (a CLABSI risk model). By identifying vulnerable patients in real time, this organization could prioritize its limited resources toward CLABSI risk reduction. Furthermore, the dashboard put information directly in front of frontline clinicians, where they had previously had to dig through Excel files and then need analysts. With the CLABSI dashboard, clinicians could quickly access utilization data from the EMR, see patients at highest risk according to real-time audits, and operationalize results.
Session 6: Closed-Loop Analytics (EHR Integration): Turning Insights into Action (Technical Session)
Jeffrey Wu, Director, Product Development, Health Catalyst
Jeffrey Wu opened his session with a story about how, when growing up, he wanted to be a pediatrician (and also Spiderman). But he took a slight detour when he went to work for a large EHR vendor, where he learned how poor information systems and bad EHRs can have a negative impact on patient care.
He then set about defining the solution of Closed-Loop Analytics, evaluating the challenges to creating closed loops in healthcare, and describing the current opportunities for Closed-Loop Analytics.
Analytics embedded within workflows enable end users (clinicians, and finance and operations leaders) to deliver better patient care, improve patient experience, and reduce costs. We need to adopt this kind of workflow or risk burning out physicians and frontline caregivers, and doing a disservice to patients.
Jeffrey shared a statistic that patient care could be 15 times more effective when information is delivered at the point of care. Closed-Loop Analytics is a conceptual cycle consisting of four linked steps where: (1) users (2) generate data (3) that is used to produce metrics, analytics, or visualizations (4) that are fed back to users to act on immediately through one or more interventions.
He also shared the Analytics Progression Model, which shows the progression from descriptive to prescriptive analytics. Most organizations get stuck on descriptive and diagnostic analytics, which inform end users about things that have already happened. The whole purpose of analytics is to use data to inform action. Action must become part of the workflow, which is the essence of Closed-Loop Analytics.
Session 7: Ambulatory Quality: Returning to the Essence of our Work (Case Study)
Neil W. Wagle, MD, MBA, Associate Chief Quality Officer, Partners HealthCare and Lara Terry, MD, MPH, Medical Director, Clinical Analytics, Partners HealthCare Center for Population Health
When looking at quality improvement efforts across the organization, Partners HealthCare realized that all data is not created equal and set out to change the way it measured performance. Fueled by feedback from staff that the existing measures were “stupid,” Partners worked to define internal measures that worked for them. In addition, they identified the tools and team needed for success, and “turbo-charged” the situation by adding feedback and motivation to drive behavior change. Part of this included tying 12 percent of PCPs’ salaries to measures, a move Wagle said he is ambivalent about because, while it drove change, it also created stress.
The results of the initiative were impressive. 1.2 million patients were impacted and 403 lives were saved. In addition, 85 percent of physicians said they believed the program had a positive impact.
Wagle and Terry shared several tips to help others create clinically meaningful measures in their organizations, including the following three critical success factors:
- Stakeholders need to be engaged early in the process
- Measures need to be embedded in existing workflow
- Data must be accurate and actionable
Session 8: Designing Hospital Quality Function Around the Value Chain to Improve Population Health (Case Study)
Leigh Hamby, MD, MHA, Chief Medical Officer, Piedmont Healthcare
Dr. Hamby opened the session with a hope of making attendees very uncomfortable about how they think their patient safety improvement program should function. He then proceeded in complete transparency to lead participants through Piedmont’s journey to improve its Patient Safety Improvement Programs at its eight facilities. After 15 years, he realized the entire program needed to be radically redesigned. After all, a system is designed to get the results it gets and they were getting very few results.
All team members were pulled together and told, “You’ve been a great baseball team… but in 90 days we are going to start playing soccer.” They were invited to apply to the new positions, but few qualified. Now armed with improvement-science-trained clinicians and 20 process engineers, magic is happening!
Hamby now prioritizes projects based entirely on his goal of zero harm and using a two-fold process of surveillance (finding the problem and discovering why it happened) and improvement design and implementation. If all you do is benchmark, all you can do is say, “We’re good enough.”
Automation and measurement are key. Hamby’s team found that by sharing measurements, physicians became motivated to change. One Piedmont facility went from 30 CAUTIs a month to zero over three months.
Hamby’s next endeavor is to decrease the time it takes to fix a problem (813 hours compared to the 5.8 hours it takes to find one). “We need to figure out how to fix problems much faster if we want to get to zero harm.”
Session 9: Agile Analytics: The Key to Improving Everything from Surgical Services to Genomic Personalized Medicine (Case Study)
Mark Poler, MD, Physician Informaticist for Enterprise Data Strategy, Division of Informatics, Geisinger Health System
Geisinger Health System has embraced the need for agile analytics in order to innovate and improve care. Dr. S. Mark Poler described the role of analytics as the integration of diverse data from incompatible sources to support analysis across clinical and business domain. Using primarily Hadoop, Dr. Poler highlighted key ingredients that health systems need to have for improvement at their organization:
- Next generation software that allows aggregation of data from a wide variety of sources, integrates data in new ways, can account for increased storage needs, and enables visual displays of data.
- Analytics center of excellence that supports process improvement by bringing the right people together to do improvement work and easily highlights the improvement work being done across the organization.
- Engaging interested parties and embracing grassroots approaches to improvement
Dr. Poler synthesized the how of what they have done at Geisinger Health Systems to include utilizing small teams that can be very agile, enterprise data management that is engaged in support and transition, innovative data management and analytics, and using modern tools that require new work paradigms.
Session 10: Using Analytics to Drive Standardization and Success in a Fixed Payment / Value-Based World (Case Study)
Chad Konchak, Sr. Director Data Analytics, NorthShore University Health System
Imagine that you need a hip replacement and you live in the United States. The average cost is $40,364. But it’s only $7,371 in Spain. This contrast is how Chad Konchak, Senior Director of Data of Analytics at NorthShore University Health System, started his presentation. Then he elaborated on the cost discrepancy by saying that you could literally fly to Spain, live in Madrid for two years, learn Spanish, run with the bulls, get trampled, get your hip replaced again, and fly home for less than the cost of a hip replacement in the U.S. Variations like this example are prevalent throughout U.S. healthcare and add to unnecessary costs and waste.
With the shift to fee-for-value, health systems like NorthShore are turning to analytics to identify where they can improve areas of waste. Using analytics doesn’t mean waiting until you have all the data you think you need, though. Chad encouraged attendees to get started with the data they had (e.g., clinical, pre-adjudicated billing, patient-reported outcomes). Chad also provided some key lessons NorthShore has learned during their journey to use analytics to drive standardization. They include:
- Be sure to test your hypothesis.
- Be careful of analysis paralysis.
- Don’t operate in silos.
- It’s critical to have physician alignment and engagement.
- Go to war with the data you have. Don’t wait until you have all the data.
- Your data needs to be enriched into actionable intelligence before it can be used for analytics.
Breakout Sessions: Wave Two
Session 11: The Enterprise Data Governance Evolution: Positioning Your Organization at the Cutting Edge of Data Quality Improvement (Case Study)
Natalie Rahming, PhD – Enterprise Data Governance Program Lead, Children’s Hospital of Philadelphia
Aware that advanced data analytics capabilities alone won’t transform healthcare, Natalie Rahming, PhD, Enterprise Data Governance Program Lead, at Children’s Hospital of Philadelphia (CHOP), launched a program to ensure effective data stewardship. Enterprise data governance (EDG) aims to provide appropriate access to data users, help them understand the data, and protect data quality.
EDG is a process for guiding behavior over definition, production, display of information, and information-related assets. Where data was previously siloed and lived on different dashboards with different numbers, EDG moves it toward greater transparency and integrity with standardized meta data. This transforms the culture of “my” data to “our” data to share across departments.
Because data is an organization’s longest living asset, governing data is critical component in success. To help CHOP realize its vision of becoming a more data- and analytics-driven organization, Dr. Rahming has worked to establish, sustain, and grow EDG.
Without data organization and management under edge, data users risk misusing data and make poor decisions. EDG protects four important principles:
- Single source of truth
The above principles help address central challenges in EDG, including lack of trust in data, little accountability and ownership, disjointed and siloed data, long wait time for data requests, minimal knowledge of data categories, lack of common business vocabulary, and inexperienced users.
Session 12: Machine Learning for Leaders: A Practical Guide to Implementing Machine Learning in Your Organization (Educational Session)
Eric Just, Senior Vice President, Clinical Analytics and Decision Support, Health Catalyst
Eric Just’s goal is to create smart consumers of machine learning because the more leaders know, the better questions they’ll be able to ask. He began with vocabulary 101, explaining the differences and similarities among machine learning, artificial intelligence, deep learning, and predictive analytics (hint: they’re mostly similar).
Eric explained some of the practical uses and specific needs of machine learning in healthcare, beginning with the millions of dollars that could be avoided in potential readmission penalties from CMS.
He offered specific guidance for organizations considering building this capability internally and those considering purchasing machine learning solutions from vendors. Both groups need to ask the right questions:
- When building machine learning models
- Do I have the right personnel to develop my models? This includes data scientists, analysts, and architects.
- How will I leverage existing open-source efforts?
- What technologies will I standardize on?
- How will my technology integrate with my analytics environment?
- When buying machine learning models
- What data set were the models trained on and how well will it match my data?
- How will I measure the accuracy of the models against my data?
- Can I retrain the model using my own data?
- How does the software integrate with my analytics environment?
Machine learning requires a data scientist, a data analyst, and a data architect/engineer. But what’s most needed is a culture that understands data. Though machine learning-based decision support may feel like it’s at the peak of 20,000-foot mountain, it’s likely more within reach than many organizations realize.
Session 13: Delivering the Healthcare Pricing Transparency That Consumers Are Demanding (Case Study)
Gene Thompson, Director, Thompson Development Ltd.
Health City Cayman Islands (HCCI) prides itself in offering high-quality, affordable care, and believes that healthcare should be commoditized not for the few, but for the all.
Using a protocol delivery system that originated in India, HCCI delivers transparent, bundled pricing. Their approach is based on a “discipline” model, which forces a focus on outcomes, reduces waste and provides a quicker pathway to patient recovery. Key aspects include:
- Transparent and predictable pricing (no CPT codes).
- A single-line billing system, which makes it easy to adjudicate claims.
- High patient volume, which drives efficiencies and increased clinical expertise.
- A real-time accounting system, which allows for daily review of metrics.
- Purchasing done in huge volumes and technology only adopted when it has a proven ROI.
- All staff are salaried employees who are incentivized to follow best practices (no FFS).
When asked how HCCI is able to deliver an affordable single-price bundle, Thompson credits their success to a relentless focus on quality, driven by repetition. He added that in HCCI’s case, they really have no other option because the majority of their patients can’t afford health care.
HCCI’s prices are one third to one fourth less than what you find in the U.S., yet the organization is currently on a mission to reduce prices even more—by up to 50 percent over the next 5 years. HCCI believes the bundled pricing model will change the way healthcare is delivered and wants to help commoditize health care around the world.
Session 14: A Universal Operating Model for Population Health Management (Educational Session)
Steve Mehran, MD, FAAP, Chief Medical Officer, Centria Healthcare
Dr. Mehran shared an eye-opening educational session that revolved around the concept of professional intimacy. The competencies required for success are not native to healthcare professionals: communication, collaboration, engagement, and orchestration. It’s not just people, process, and technology… it’s presence.
Taking transcendent responsibility for the health status of a cohort or community requires a completely different operating model than caring for patients who “self-select” for care based on their own assessment of their condition.
Dr. Mehran reviewed the essential triad for population management to be a successful, systems-based practice.
- Functional requirements: All eight of the functional requirements must be fulfilled. However, individual organizations may fulfill them differently.
- Operating capabilities: There are seven core areas that must be examined: organizational capacity; workforce readiness; clinical processes and operations; patient experience management; clinical technology systems; data, analytics and reporting; finance/business models.
- Interaction design: Just because you can talk about population health does not mean you can do it. Population management requires orchestrating and optimizing goal-directed collaboration, operating relationships, and knowledge management.
For collaboration to function as a formal discipline, a shared meaning of collaboration needs to exist. Centria Healthcare defines it as “Mutually beneficial relationship between individuals or organizations who work toward common goals by sharing responsibility, authority, and accountability for achieving results.”
In closing, Dr. Mehran expressed his belief that patients prefer to be cared for rather than empowered. It is likely that when a patient feels truly cared for, they will find the intrinsic motivation to be accountable for their own health.
15: How to Use Machine Learning to Improve Outcomes (Technical Session)
Levi Thatcher, PhD, Vice President Data Science, Health Catalyst
Pinterest, Facebook, and Google Maps all use machine learning and the healthcare industry is in critical need of catching up in order to improve outcomes. Levi Thatcher wants to help take healthcare from a reactive and retrospective industry into the predictive and prescriptive world of machine learning.
Key elements in using machine learning to improve outcomes include understanding the business problem, involving subject matter experts who can inform the kind of data you need to pull in to your algorithm, and having the analysts, data, and tools to implement machine learning.
There are 4 main steps to incorporate machine learning into outcomes improvement:
- Choose a business problem. Be sure to consider who will be impacted by this problem and the improvement goal that is focused on solving this problem.
- Organize a dataset. Start with defining and engineering the population, outcome variable, and predictive features.
- Develop and deploy a model. Train, test, and tune the model and then apply the best model to new data.
- Surface the insight and guidance. Combine predictions with context and interventions to produce actionable insight.
Levi highlighted that often data analysts show talent and aptitude for machine learning because they are good at building features, which is about 75% of the effort in engineering predictive features of a dataset.
Session 16 – Dedication to Quality Improvement Delivers on the Triple Aim: Saves Tens of Millions Annually (Case Study)
Nicole Kveton, RN, BSN, MHA, Vice President, Allina Health Group Quality, Value and Nursing, Allina Health; and Sue Fairchild, Program Manager, Allina Health
Allina Health believes that “improving value requires improving one or more outcomes without raising costs, or lowering costs without compromising outcomes, or both.” But they recognize that accomplishing this objective isn’t an easy thing to do. It requires working together as a system to survive the shifting sands of our industry.
In its ongoing quest to improve quality, Allina Health created the Improving Clinical Value (ICV) process. This process enables a multidisciplinary team approach across the entire system. Since beginning to implement ICV, they’ve been able to better prioritize the great work that teams across Allina are doing to standardize care, improve outcomes, and account for tens of millions of dollars annually. Some examples include:
- The Women’s Health initiative that has resulted in $210,000 in increased revenue, a 20.8% relative improvement in no-show rate, a 20% increase in available ultrasound appointments, and an 18.2% increase in utilization.
- A minimalist approach to TAVR which led to $1.1 million savings in less than one year, a 31% relative reduction in length of stay, and a 17.4% relative reduction in median procedure time.
- Heparin improvement work that led to a 7% relative improvement in the percentage of patients therapeutic within 24 hours of protocol initiation and a paring of 20+ site-based documents to one systemwide guideline and four systemwide protocols.
HAS 17 Edition of “Hollywood Squares”
Tom Burton, Co-Founder and Senior Vice President, Health Catalyst
How do you conduct three simultaneous healthcare panel discussions on the same stage while keeping everything organized, engaging, and downright entertaining? Bring in the “Chief Fun Officer,” Tom Burton, who emceed the HAS version of “Hollywood Squares” (remember the old TV game show that debuted in the 1960s?). Instead of entertainment celebrities, however, Tom presented nine data “celebrities,” each an expert in one of three important healthcare topics:
- Population health management.
- Technology in healthcare.
- Financial transformation in healthcare.
Two previously selected contestants join Burton on stage to agree or disagree with panelist answers to pertinent healthcare questions. The audience played along through the HAS 17 app, where they entered their agreement or disagreement with the contestant. Questions included: What are the top two diagnoses among Medicare patients? And, how much healthcare data does the government maintain for each person in the U.S.? Panelists then explained the real answer to each question.
But everyone was a winner because, disguised behind all the fun, the audience learned some really helpful tips, such as why higher quality healthcare actually costs less (it’s more organized, disciplined, and strategic); what are the top two diagnoses in dealing with Medicaid patients (substance abuse and mental illness); why our country cannot continue investing in healthcare at the current rate (it’s bankrupting us); and what is the quickest way to create data confusion (lack of effective data governance).
The “Secret Square” was revealed with the final celebrity pick, but the overall game ended in a tie that was broken with a sophisticated game of “Rock, Paper, Scissors.”
Thanks to the celebrity stars (Amy Flaster, Regina Bergman, Christopher Kodama, Jon Russell, Dale Sanders, Chris Harper, Gene Thompson, Duncan Gallagher, and Robert DiMichiei), TV gameshows reenacted at healthcare analytics events by healthcare leaders will never be the same.
And that ended Wednesday’s presentations at HAS 17.
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