DKA Risk Prediction Tool Helps Reduce Hospitalizations

Each year, more than 12,700 pediatric patients are diagnosed with diabetic ketoacidosis (DKA), a life threatening complication of diabetes. Texas Children’s Hospital sought a way to accurately predict risk of DKA in time for care team members to intervene before these patients suffered a severe episode.

The health system ultimately formed a multidisciplinary high risk diabetes team to devise pre- and post-discharge strategies, and DKA risk prediction tools aided by the Health Catalyst Analytics Platform built using the Late-BindingTM Data Warehouse.

Results:

  • 30.9 percent relative reduction in recurrent DKA admissions per fiscal year.
  • 90 percent of all patients with new onset type 1 diabetes at the Medical Center Campus have a documented RIPGC in their medical chart.
  • 100 percent of patients with type 1 diabetes have a risk index for DKA documented every 6 months.
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Other Content in Machine Learning / Predictive Analytics

Posts

Health Catalyst Introduces catalyst.ai: Machine Learning in Healthcare Is Now for Everyone

Despite its prevalence in many other industries (and its use by most Americans every single day), machine learning in healthcare is far behind. But not for long, because Health Catalyst® is bringing this life-saving technology to healthcare with catalyst.ai™—a new machine learning technology initiative that helps healthcare organizations of any size use predictive analytics to transform healthcare.

The clinical, operational, and financial opportunities catalyst.ai gives health systems are limitless:

  • Prevent hospital acquired infections.
  • Predict chronic disease.
  • Reduce readmissions
  • Reduce hospital Length-of-Stay.
  • Predict propensity-to-pay.
  • Predict no-shows.

Catalyst.ai™ (machine learning models built into every Health Catalyst application) together with healthcare.ai™ (a collaborative, open source repository of standardized machine learning methodologies and production-quality code that makes it easy to deploy machine learning in any environment), represents a new era of powerful predictive analytics that will not only improve outcomes, but also save lives.

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How Healthcare AI Makes Machine Learning Accessible to Everyone in Healthcare

Before the introduction of healthcare.ai, an open source, healthcare-specific machine learning software, only a small subset of healthcare staff (primarily data scientists) had the ability to leverage predictive analytics to improve outcomes.

Healthcare.ai will democratize machine learning by empowering everyone in healthcare with the appropriate technical skills (BI developers, project managers, data architects, etc.) to download the healthcare.ai tools (packages for R and Python), request features, ask questions, and contribute code.

What sets healthcare.ai apart from other machine learning tools is its healthcare-specific functionality:

  • Pays attention to longitudinal questions.
  • Offers an easy way to do risk-adjusted comparisons.
  • Provides easy connections and deployment to databases.

Healthcare.ai will do more than just democratize machine learning—it will transform healthcare.

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Prospective Analytics: The Next Thing in Healthcare Analytics

Retrospective and predictive analytics are familiar terms for practitioners of clinical outcomes improvement, but the new kid on the block is prospective analytics. This is the next level that uses findings from its predecessors to not only identify the best clinical routes, but also what the results might be of each choice. Prospective analytics gives bedside clinicians an expanded, branching view of operational and clinical options in a type of decision support that can lead to not only improving surgical and medical outcomes, but to making a positive financial contribution, as well. But, as expected with any new process or new way of thinking, prospective analytics requires careful introduction and stewardship to help drive its adoption within the organization.

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Patient Flight Path Analytics: From Airline Operations to Healthcare Outcomes

We developed a predictive analytics framework for patient care based upon concepts from airline operations. Using the idea of an aircraft turnaround time where the airline wants to put the aircraft back into operation as soon as possible, we’ve created a way to help patients headed toward poor outcomes, along with their providers, “turnaround” and get the best possible, most cost-effective outcome. For example, in a diabetes patient, we might use variables such as: age, alcohol use, annual eye/foot exam, BMI, etc. to look for patterns that might influence two outcomes: 1) Diabetic control and 2) The absence of progression toward diabetic complications. The notion of our Patient Flight Path is useful at both the conceptual level, as well as the predictive algorithm implementation level.

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The Power of Geo-Analytics (and Maps) to Improve Predictive Analytics in Healthcare

As far back as the 1840s, clinicians have been using maps to inform them about population health trends. Today, the geo-analytics industry is well-developed in almost every application, with the exception of healthcare and medicine. There is potential to use mapping technologies to show patient disease burden in geographic form, map locations of health care facilities, and a plethora of accountable care population health initiatives would benefit from geo-analysis. Health Catalyst is working to integrate inputs into analysis like maps that can show geographic care boundaries, population health demographics, and more.

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Webinars

The MD Anderson / IBM Watson Announcement: What Does It Mean For Machine Learning In Healthcare?

It’s been over six years since IBM’s Watson amazed all of us on Jeopardy, but it has yet to deliver similar breakthroughs in healthcare. The headlines in last week’s Forbes article read, “MD Anderson Benches IBM Watson In Setback For Artificial Intelligence In Medicine.” Is it really a setback for the entire industry or not? Health Catalyst’s EVP for Product Development, Dale Sanders, believes that the challenges are unique to IBM’s machine learning strategy in healthcare. If they adjust that strategy and better manage expectations about what’s possible for machine learning in medicine, the future will be brighter for Watson, their clients, and AI in healthcare, in general. Watson’s success is good for all of us, but it’s failure is bad for all of us, too.

Join Dale as he discusses:

  1. The good news: Machine learning technology is accelerating at a rate beyond Moore’s Law. Dale believes that machine learning algorithms and models are doubling in capability every six months.
  2. The bad news: The healthcare data ecosystem is not nearly as rich as many would believe, and certainly not as rich as that used to train Watson for Jeopardy. Without high-volume, high-quality data, Watson’s potential and the constant advances in machine learning algorithms will hit a glass ceiling in healthcare.
  3. The best news: By adjusting strategy and expectations, there are still plenty of opportunities to do great things with machine learning by using the current data content in healthcare, while we build out the volume and breadth of data we need to truly understand the patient at the center of the healthcare picture… and you don’t need an army of PhD data scientists to do it.
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Deploying Predictive Analytics in Healthcare

This webinar will focus on the technical and practical aspects of creating and deploying predictive analytics. We have seen an emerging need for predictive analytics across clinical, operational, and financial domains. One pitfall we’ve seen with predictive analytics is that while many people with access to free tools can develop predictive models, many organizations fail to provide a sufficient infrastructure in which the models are deployed in a consistent, reliable way and truly embedded into the analytics environment. We will survey techniques that are used to get better predictions at scale. This webinar won’t be an intense mathematical treatment of the latest predictive algorithms, but will rather be a guide for organizations that want to embed predictive analytics into their technical and operational workflows.

Topics will include:

  1. Reducing the time it takes to develop a model
  2. Automating model training and retraining
  3. Feature engineering
  4. Deploying the model in the analytics environment
  5. Deploying the model in the clinical environment
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There Is A 90% Probability That Your Son Is Pregnant: Predicting the Future of Predictive Analytics in Healthcare

In this webinar, which is geared for managers and executives, Dale Sanders provides a new version of a very popular lecture he presented at this year’s Health Analytics Summit in Salt Lake City. Attendees will gain an understanding of:

  • What to expect from predictive analytics as it relates to human behavior
  • A general overview of predictive analytics models, and the contexts in which those various models should and should not be used
  • The scenarios in which predictive models in healthcare are effective and when they are not, given that 80% of population health outcomes are determined by socio-econonic factors, not healthcare delivery
  • The relationship between predictive analytic accuracy and topics of data management such as data quality, data volume and patient outcomes data
  • The use of predictive analytics to identify patients who are on a trajectory for poor, as well as good, outcomes
  • How current predictive analytics strategies are overlookng the cost of intervention and “Return on Engagement”, ROE— the cost per unit of healthcare improvement for patient populations
  • The cultural, philosophical, and legal conundrums that predictive analytics will create for healthcare, notably healthcare rationing

The success of predictive analytics will not be defined by the simple risk stratification of patient populations for care management teams. Success will depend on the costs of intervention to reduce the risks that are identified by predictive analytics, which boils down to this two-part question: Now that we can predict a patient’s risk for a bad healthcare outcome, “What’s the probability of influencing this patient’s behavior towards a better outcome?” And, “How much effort and cost will be required for that influence?”

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Predictive Analytics: It’s the Intervention That Matters (Webinar)

In this two-part webinar, get the detailed knowledge you need to make informed decisions about adopting predictive analytics in healthcare so you can separate today’s hype from reality. In part 1, you’ll learn from Dale Sanders:

  1. Our fixation on predictive analytics in readmissions,
  2. The common trap of predictions without interventions,
  3. The common misconceptions of correlations verses causation,
  4. Examples of predictions without algorithms, and
  5. The importance of putting the basics first.

In part 2, you’ll hear from industry expert David Crockett, PhD in a “graduate level” crash course cover key concepts such as machine learning, algorithms, feature selection, classification, tools and more.

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