H2O.ai Case Studies Machine Learning to Save Lives

Edit This Case Study Record

	Machine Learning to Save Lives H2O.ai

Machine Learning to Save Lives

H2O.ai

Technology Category	Analytics & Modeling - Machine Learning Analytics & Modeling - Predictive Analytics
Applicable Industries	Healthcare & Hospitals
Applicable Functions	Quality Assurance
Use Cases	Predictive Maintenance Remote Patient Monitoring
Services	Data Science Services
Challenge	Kaiser Permanente, an integrated healthcare system, was facing a challenge with patients who undergo an unplanned transfer to the Intensive Care Unit (ICU). These patients experienced significantly worse outcomes than those directly admitted to the ICU. They represented about a fourth of all Kaiser ICU admissions, a fifth of all deaths in the hospital, and about an eighth of all of the hospital days. The patients who experienced an unplanned transfer to the ICU experienced two to five times the mortality of patients who are directly admitted to the ICU, and they would stay in the hospital an average of 8 to 12 days more than patients who are directly admitted to the ICU. The challenge was to identify these patients ahead of time who are likely to crash and be rushed to the ICU, and intervene before the deterioration. Read More
About Customer	Kaiser Permanente is an integrated healthcare system, where patients receive a variety of services and treatments under one roof. With 10 million members, Kaiser Permanente has vast amounts of data, and is using innovative approaches to make this data work for the benefit of its patients and clinicians. Kaiser has always been on the forefront of using technology – as early as 1970s, the company began keeping electronic medical records. Today, Kaiser members have a variety of methods available to them to interact with their doctors and nurses – using the web, e-mail and mobile apps to get the answers they need faster. All this data is captured and can be used to solve specific problems. Read More
Solution	Kaiser decided to use available data about a patient and build a mechanism that could identify when deterioration might occur. They developed the Advanced Alert Monitoring (AAM) System, which helps identify patients who are likely to crash, and they recognize them 12 hours before they experience the deterioration. The AAM has four components: risk analysis and the creation of the score, monitoring and warning, dissemination and communication of the scope, and finally – the intervention. They used bed history data, chemistry, vital signs, comorbidities, and demographic information to estimate either the probability of a late transferring to the ICU that is a sudden crash or mortality in the hospital. Once the data is collected and cleaned, the team worked on feature engineering and then applied a variety of training and testing datasets. The team explored each parameter and its linkage, got the clinicians involved to look at every case, and then performed a characteristics review and threshold analysis to make sure that a threshold made sense. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Machine Learning

Analytics & Modeling - Predictive Analytics

Applicable Industries

Healthcare & Hospitals

Applicable Functions

Quality Assurance

Use Cases

Predictive Maintenance

Remote Patient Monitoring

Services

Data Science Services

Challenge

Kaiser Permanente, an integrated healthcare system, was facing a challenge with patients who undergo an unplanned transfer to the Intensive Care Unit (ICU). These patients experienced significantly worse outcomes than those directly admitted to the ICU. They represented about a fourth of all Kaiser ICU admissions, a fifth of all deaths in the hospital, and about an eighth of all of the hospital days. The patients who experienced an unplanned transfer to the ICU experienced two to five times the mortality of patients who are directly admitted to the ICU, and they would stay in the hospital an average of 8 to 12 days more than patients who are directly admitted to the ICU. The challenge was to identify these patients ahead of time who are likely to crash and be rushed to the ICU, and intervene before the deterioration.

About Customer

Kaiser Permanente is an integrated healthcare system, where patients receive a variety of services and treatments under one roof. With 10 million members, Kaiser Permanente has vast amounts of data, and is using innovative approaches to make this data work for the benefit of its patients and clinicians. Kaiser has always been on the forefront of using technology – as early as 1970s, the company began keeping electronic medical records. Today, Kaiser members have a variety of methods available to them to interact with their doctors and nurses – using the web, e-mail and mobile apps to get the answers they need faster. All this data is captured and can be used to solve specific problems.

Solution

Kaiser decided to use available data about a patient and build a mechanism that could identify when deterioration might occur. They developed the Advanced Alert Monitoring (AAM) System, which helps identify patients who are likely to crash, and they recognize them 12 hours before they experience the deterioration. The AAM has four components: risk analysis and the creation of the score, monitoring and warning, dissemination and communication of the scope, and finally – the intervention. They used bed history data, chemistry, vital signs, comorbidities, and demographic information to estimate either the probability of a late transferring to the ICU that is a sudden crash or mortality in the hospital. Once the data is collected and cleaned, the team worked on feature engineering and then applied a variety of training and testing datasets. The team explored each parameter and its linkage, got the clinicians involved to look at every case, and then performed a characteristics review and threshold analysis to make sure that a threshold made sense.

Impact #1	Clinicians receive an alert if a threshold is exceeded to evaluate the patient and determine further course of action
Impact #2	Intervention prior to deterioration creates better outcomes for the patient
Impact #3	The results are currently available every six hours, but AAM can be configured to calculate the likelihood of critical deterioration on an hourly basis

Impact #1

Clinicians receive an alert if a threshold is exceeded to evaluate the patient and determine further course of action

Impact #2

Intervention prior to deterioration creates better outcomes for the patient

Impact #3

The results are currently available every six hours, but AAM can be configured to calculate the likelihood of critical deterioration on an hourly basis

Benefit #1	Patients who experience an unplanned transfer to the ICU experience two to five times the mortality of patients who are directly admitted to the ICU
Benefit #2	Patients who experience an unplanned transfer to the ICU would stay in the hospital an average of 8 to 12 days more than patients who are directly admitted to the ICU

Benefit #1

Patients who experience an unplanned transfer to the ICU experience two to five times the mortality of patients who are directly admitted to the ICU

Benefit #2

Patients who experience an unplanned transfer to the ICU would stay in the hospital an average of 8 to 12 days more than patients who are directly admitted to the ICU

Download PDF Version

Overview

Machine Learning to Save Lives

Operational Impact

Quantitative Benefit