This is an excerpt of the report below, focusing on the topic above. The publication is preceded by the key messages about the theme.
Healthcare’s Data Readiness Crisis
HEALTHCARE IT IN 2022: Triage vs. Transformation
Morning Consult survey for Innovaccer
April 2022
Excerpted by
Joaquim Cardoso MSc.
The Health Revolution Institute
Digital Health Unit
April 11, 2022
Key messages
It’s widely accepted that AI is pivotal to multiple digital transformation objectives.
- But only 1 in 3 respondents said their AI capabilities were sufficient to support their digital transformation.
- This represents a confusing disconnect between what the healthcare industry needs to drive digital transformation and what it’s spending its money on.
- It’s essential to figure out your data strategy now, because data readiness is the lifeblood to unlocking the potential of AI and solving so many other healthcare challenges we’re facing now
Healthcare IT execs are concerned that the hard costs of AI won’t justify the achievable benefits. And it makes sense, given the data readiness crisis.
- AI can’t live up to its promise when data remains fragmented.
- It’s the age-old “garbage-in, garbageout” problem that AI and machine learning have always faced.
ORIGINAL PUBLICATION (excerpt, focused on AI)
Healthcare’s Data Readiness Crisis
HEALTHCARE IT IN 2022: Triage vs. Transformation
Morning Consult survey for Innovaccer
April 2022
Most are aiming to achieve digital transformation by 2025
- The data readiness crisis is a big deal for 95% of healthcare executives, who are working on digital transformation right now.
- 61% are targeting their organizations to achieve full digital transformation in just three to five years.
- 43% hope to achieve their digital transformation goals within three years.
- Two in five want to achieve digital transformation in two years or less.
The shift is on. This means now is the time to tackle data readiness and lay the foundation for systemic interoperability.
AI is pivotal to driving real-term value both in near-term
Healthcare leaders surveyed agreed that AI has enormous potential to help drive better clinical outcomes, operational performance, systemic efficiency, financial and administrative performance, and consumer engagement
AI is pivotal to long-term transformation, especially clinical outcomes
But a lack of data readiness is holding us back in a big way
- It’s widely accepted that AI is pivotal to multiple digital transformation objectives.
- But only 1 in 3 respondents said their AI capabilities were sufficient to support their digital transformation.
- The rest aren’t ready or aren’t sure if they are.
- The industry still seems to be figuring this out.
- It’s essential to figure out your data strategy now, because data readiness is the lifeblood to unlocking the potential of AI and solving so many other healthcare challenges we’re facing now
It’s widely accepted that AI is pivotal to multiple digital transformation objectives.
But only 1 in 3 respondents said their AI capabilities were sufficient to support their digital transformation.
It’s essential to figure out your data strategy now, because data readiness is the lifeblood to unlocking the potential of AI and solving so many other healthcare challenges we’re facing now
Near-term investment in AI is lackluster, at best
- Despite the vital role healthcare leaders said AI plays in near- and long-term transformation of clinical, operational, financial, and administrative outcomes, near-term investment in AI is surprisingly low.
- The vast majority of respondents said they were deferring investment in AI to sometime in 2025.
- This represents a confusing disconnect between what the healthcare industry needs to drive digital transformation and what it’s spending its money on.
The vast majority of respondents said they were deferring investment in AI to sometime in 2025.
This represents a confusing disconnect between what the healthcare industry needs to drive digital transformation and what it’s spending its money on.
The problem: Skepticism that AI will live up to its potential
- Healthcare IT execs are concerned that the hard costs of AI won’t justify the achievable benefits. And it makes sense, given the data readiness crisis.
- AI can’t live up to its promise when data remains fragmented.
- It’s the age-old “garbage-in, garbageout” problem that AI and machine learning have always faced.
AI can’t live up to its promise when data remains fragmented.
It’s the age-old “garbage-in, garbageout” problem that AI and machine learning have always faced.
The solution: Invest in data readiness and systemic interoperability
THINK ABOUT IT — without enterprise data readiness, any AI deployment is going to be fractured, incomplete, and irrelevant. Nobody would trust it.
Yet AI remains a central component of any digital transformation strategy and has the potential to yield immediate and longterm benefits for clinical, operational, financial, and administrative performance.
Despite the perceived cost of investing in AI, the cost of not investing in AI is even more dire.
So … what if you could achieve the full potential of AI and lower the cost of innovation by investing in technologies that drive data readiness and systemic interoperability?
As healthcare IT executives think through their priorities, many still seem torn between focusing on near-term or long-term goals.
The truth is, it’s possible (and, indeed, essential) to deal with both at the same time.
