HIMSS TV is your Insider’s Guide to everything HIMSS. We are the world’s first online broadcasting network, focused on global innovation and how information and technology are driving change in healthcare.
  Home

AI/ML (654)

6:09

Bundling predictive and descriptive analytics to create patient risk scores

Westchester Medical Center integrated data from the EHR using a machine learning algorithm to connect patients to the right sources of care, according to Simer Sodhi, the organization's director of data management & analytics.

Bundling predictive and descriptive analytics to create patient risk scores
Westchester Medical Center integrated data from the EHR using a machine learning algorithm to connect patients to the right sources of care, according to Simer Sodhi, the organization's director of data management & analytics.
6:09
6:32

Digital health 'sherpas' helping guide the journey of bringing AI to healthcare

Dr. Sujay Kakarmath, digital health scientist at Partners HealthCare Pivot Labs, explains how the Boston organization is enabling collaboration between hospitals, health systems and other stakeholders such as big tech and pharma.

Digital health 'sherpas' helping guide the journey of bringing AI to healthcare
Dr. Sujay Kakarmath, digital health scientist at Partners HealthCare Pivot Labs, explains how the Boston organization is enabling collaboration between hospitals, health systems and other stakeholders such as big tech and pharma.
6:32
5:56

How blockchain and AI can work together for healthcare

Tej Anand, professor of practice at the University of Texas' McCombs School of Business, explains how the most promising use case for blockchain in healthcare is the elimination of "redundant work, rework and reconciliation."

How blockchain and AI can work together for healthcare
Tej Anand, professor of practice at the University of Texas' McCombs School of Business, explains how the most promising use case for blockchain in healthcare is the elimination of "redundant work, rework and reconciliation."
5:56
4:07

AI in healthcare has come a long way, and quickly - where's it going next?

Badrul Husain, CEO of Infolytx, explains how his company's natural language processing technology is helping with clinical trial cohorts, and discusses how AI is gaining real traction elsewhere across healthcare.

AI in healthcare has come a long way, and quickly - where's it going next?
Badrul Husain, CEO of Infolytx, explains how his company's natural language processing technology is helping with clinical trial cohorts, and discusses how AI is gaining real traction elsewhere across healthcare.
4:07
7:26

AI: Reality vs. hype

(Sponsored) Kevin Shah, head of Enterprise New Business at Fujifilm Europe, says automating some processes can deliver diagnoses more quickly, increasing the chance of better outcomes for patients while reducing clinicians' workloads.

AI: Reality vs. hype
(Sponsored) Kevin Shah, head of Enterprise New Business at Fujifilm Europe, says automating some processes can deliver diagnoses more quickly, increasing the chance of better outcomes for patients while reducing clinicians' workloads.
7:26
4:51

Child welfare, behavioral health pose unique analytics challenges

KVC Health Systems CIO Lonnie Johnson describes the IT and data strategies of his multi-state provider of foster care and adoption services, including its predictive modeling goals and first steps toward AI and machine learning.

Child welfare, behavioral health pose unique analytics challenges
KVC Health Systems CIO Lonnie Johnson describes the IT and data strategies of his multi-state provider of foster care and adoption services, including its predictive modeling goals and first steps toward AI and machine learning.
4:51
5:25

Next frontier of analytics: Applying ML and AI to clinical care guidance

Interventions are the hardest part as disparate data has to be interwoven for behavior change if risk is to be mitigated, says Duke University Health System Chief Analytics Officer Stephen Blackwelder.

Next frontier of analytics: Applying ML and AI to clinical care guidance
Interventions are the hardest part as disparate data has to be interwoven for behavior change if risk is to be mitigated, says Duke University Health System Chief Analytics Officer Stephen Blackwelder.
5:25
6:07

Why building an analytics team is like starting a garage band

The challenge is not so much in the technology development but in the cultural maturity for people to realize what the data can do for them, says Chad Konchak, assistant vice president of Clinical Analytics at NorthShore University HealthSystem.

Why building an analytics team is like starting a garage band
The challenge is not so much in the technology development but in the cultural maturity for people to realize what the data can do for them, says Chad Konchak, assistant vice president of Clinical Analytics at NorthShore University HealthSystem.
6:07
2:20

Health organizations focused on getting ML and AI into workflows

Rob Wellen, regional vice president at KenSci, talks about common data analytics questions from providers, including how to operationalize machine learning and AI.

Health organizations focused on getting ML and AI into workflows
Rob Wellen, regional vice president at KenSci, talks about common data analytics questions from providers, including how to operationalize machine learning and AI.
2:20
3:05

IT has role of supporting and empowering clinicians to provide better care

It starts with engaging the clinicians and having a team to standardize care to reduce variation for measurable changes, says Srinivasan Suresh, MD, VP at UPMC Children's Hospital of Pittsburgh.

IT has role of supporting and empowering clinicians to provide better care
It starts with engaging the clinicians and having a team to standardize care to reduce variation for measurable changes, says Srinivasan Suresh, MD, VP at UPMC Children's Hospital of Pittsburgh.
3:05
2:53

Be open: Analytics and machine learning is a journey

Michael Schwarz, executive director of IS at Indiana University Health System, says start with an understanding of what the data quality issues are and the differences in documentation and in workflow from different providers.

Be open: Analytics and machine learning is a journey
Michael Schwarz, executive director of IS at Indiana University Health System, says start with an understanding of what the data quality issues are and the differences in documentation and in workflow from different providers.
2:53
4:52

Predictive analytics drives value-based care by identifying high-risk patients

Advocate Aurora Health’s data and analytics heart failure pilot realized a 23 percent reduction in utilization, says Tina Esposito, the company’s chief health information officer.

Predictive analytics drives value-based care by identifying high-risk patients
Advocate Aurora Health’s data and analytics heart failure pilot realized a 23 percent reduction in utilization, says Tina Esposito, the company’s chief health information officer.
4:52
of 55
Play video Bundling predictive and descriptive analytics to create patient risk scores
AI/ML
Bundling predictive and descriptive analytics to create patient risk scores
Play video Digital health 'sherpas' helping guide the journey of bringing AI to healthcare
AI/ML
Digital health 'sherpas' helping guide the journey of bringing AI to healthcare
Play video How blockchain and AI can work together for healthcare
AI/ML
How blockchain and AI can work together for healthcare
Play video AI in healthcare has come a long way, and quickly - where's it going next?
AI/ML
AI in healthcare has come a long way, and quickly - where's it going next?

View more in AI/ML (654 videos)

7:26

AI: Reality vs. hype

(Sponsored) Kevin Shah, head of Enterprise New Business at Fujifilm Europe, says automating some processes can deliver diagnoses more quickly, increasing the chance of better outcomes for patients while reducing clinicians' workloads.

AI: Reality vs. hype
(Sponsored) Kevin Shah, head of Enterprise New Business at Fujifilm Europe, says automating some processes can deliver diagnoses more quickly, increasing the chance of better outcomes for patients while reducing clinicians' workloads.
7:26
4:51

Child welfare, behavioral health pose unique analytics challenges

KVC Health Systems CIO Lonnie Johnson describes the IT and data strategies of his multi-state provider of foster care and adoption services, including its predictive modeling goals and first steps toward AI and machine learning.

Child welfare, behavioral health pose unique analytics challenges
KVC Health Systems CIO Lonnie Johnson describes the IT and data strategies of his multi-state provider of foster care and adoption services, including its predictive modeling goals and first steps toward AI and machine learning.
4:51
5:25

Next frontier of analytics: Applying ML and AI to clinical care guidance

Interventions are the hardest part as disparate data has to be interwoven for behavior change if risk is to be mitigated, says Duke University Health System Chief Analytics Officer Stephen Blackwelder.

Next frontier of analytics: Applying ML and AI to clinical care guidance
Interventions are the hardest part as disparate data has to be interwoven for behavior change if risk is to be mitigated, says Duke University Health System Chief Analytics Officer Stephen Blackwelder.
5:25
6:07

Why building an analytics team is like starting a garage band

The challenge is not so much in the technology development but in the cultural maturity for people to realize what the data can do for them, says Chad Konchak, assistant vice president of Clinical Analytics at NorthShore University HealthSystem.

Why building an analytics team is like starting a garage band
The challenge is not so much in the technology development but in the cultural maturity for people to realize what the data can do for them, says Chad Konchak, assistant vice president of Clinical Analytics at NorthShore University HealthSystem.
6:07
2:20

Health organizations focused on getting ML and AI into workflows

Rob Wellen, regional vice president at KenSci, talks about common data analytics questions from providers, including how to operationalize machine learning and AI.

Health organizations focused on getting ML and AI into workflows
Rob Wellen, regional vice president at KenSci, talks about common data analytics questions from providers, including how to operationalize machine learning and AI.
2:20
3:05

IT has role of supporting and empowering clinicians to provide better care

It starts with engaging the clinicians and having a team to standardize care to reduce variation for measurable changes, says Srinivasan Suresh, MD, VP at UPMC Children's Hospital of Pittsburgh.

IT has role of supporting and empowering clinicians to provide better care
It starts with engaging the clinicians and having a team to standardize care to reduce variation for measurable changes, says Srinivasan Suresh, MD, VP at UPMC Children's Hospital of Pittsburgh.
3:05
2:53

Be open: Analytics and machine learning is a journey

Michael Schwarz, executive director of IS at Indiana University Health System, says start with an understanding of what the data quality issues are and the differences in documentation and in workflow from different providers.

Be open: Analytics and machine learning is a journey
Michael Schwarz, executive director of IS at Indiana University Health System, says start with an understanding of what the data quality issues are and the differences in documentation and in workflow from different providers.
2:53
4:52

Predictive analytics drives value-based care by identifying high-risk patients

Advocate Aurora Health’s data and analytics heart failure pilot realized a 23 percent reduction in utilization, says Tina Esposito, the company’s chief health information officer.

Predictive analytics drives value-based care by identifying high-risk patients
Advocate Aurora Health’s data and analytics heart failure pilot realized a 23 percent reduction in utilization, says Tina Esposito, the company’s chief health information officer.
4:52