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UC Berkeley’s Ziad Obermeyer Is Optimistic About Algorithms

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As an associate professor at University of California, Berkeley, Dr. Ziad Obermeyer has made waves throughout the healthcare informatics industry with his work on machine learning, public policy and computational medicine.

In 2019, he was the lead author on a paper published in Science showing that a widely used population health algorithm exhibits significant racial bias.

In recent years, the subject of identifying and confronting bias in machine learning has continued to emerge in healthcare spaces.

Obermeyer, who will present at the HIMSS Machine Learning for AI and Healthcare event next week – alongside Michigan State University Assistant Professor Mohammad Ghassemi, Virginia Commonwealth University Assistant Professor Shannon Harris and HIMSS Outside Counsel Karen Silverman – sat down with Healthcare IT News to discuss how stakeholders can take bias into consideration when developing algorithms and why he feels optimistic about artificial intelligence.

Q. Could you tell me a bit about your background when it comes to studying bias in machine learning?

A. I came to this work, in many ways, from a place of great optimism about what artificial intelligence can and will do for medicine. So a lot of the work that led to the work on bias was actually trying to build algorithms that work well and generally do what we want them to do, and not reinforce structural inequalities and racism. You know, I still actually have a lot of that optimism.

But I think we need to be so careful along the way toward that vision of an artificial intelligence that helps doctors and other decision-makers of health do their jobs better and serve the people they need to serve.

That’s kind of the overriding message that I try to stick to in my work: This is really going to transform medicine and healthcare for the better, as long as we are so careful and aware of all of the places that it can go wrong.

Q. And how can stakeholders and developers – and also providers – be careful in that way? What should they be taking into consideration when they’re relying on artificial intelligence to treat patients?

A. We got a lot of publicity for some of our work on bias. And what we tried to do is turn that publicity into collaborations with a lot of organizations in health, whether they were insurers, or healthcare systems, or even technology companies.

We learned some lessons from that very applied work that I think are really important for everyone who is working in this area to keep in mind.

Maybe it sounds a little trite, but the most important thing is to know what you actually want the algorithm to be doing. What is the decision that we’re trying to improve? Who is making that decision? What is the information that the algorithm should be providing to that person to help her make a decision better?

Even though it sounds so obvious, that is often missing from the way that we build algorithms. It often starts from, “Oh, I have this data, what can I do with it?” or these putting the cart before the horse situations.

I think that’s really the first and most important place to start – to really try to articulate exactly what we want the algorithm to be doing and then hold it accountable for that.

That’s where we started when we did our initial work, which was: OK, we want all of these population health management algorithms to be helping us understand who’s sick. That’s what we want to be doing. But what are the algorithms actually doing? Well, they’re predicting who’s going to cost money.

And even though those two things are related, they’re actually quite different, especially for non-white people, and poor people, and rural people, and anyone who lacks access to or is treated differently by the healthcare system.

I think that [question of algorithmic purpose] is easy to say, but it’s much harder to do because it requires you to really understand the context in which algorithms are operating, understand where the data comes from, understand how structural biases can work their way into the data and then work around them.

One of the really important things that I learned from this work is that, even though we’ve found bias now way beyond that initial algorithm – almost everywhere we’ve looked in the healthcare system, through these partnerships – we’ve also found that bias can be fixed if we are aware of it, and we work around it when building algorithms.

When we do that, we turn algorithms from tools that reinforce all of these ugly things about our healthcare system into tools that are just and equitable and do what we want them to do, which is help sick people.

Q. One thing I’ve been wondering about is bias in application. Even if an algorithm were set up to be as neutral as possible, are there implementations that could be using it in biased ways? How could organizations guard against that?

A. Let’s imagine that you were a profit-maximizing insurance company. It’s still not the case that you would build an algorithm that predicts total costs because total costs are not avoidable costs.

And if you start thinking carefully about what avoidable costs are and where they come from, in our healthcare system, even those kinds of costs are going to be concentrated in the most disadvantaged people, because who doesn’t go to their primary care doctor because they can’t get the day off of work? Or because they can’t afford the copay? Who are the people who had a heart attack hospitalization that could have been prevented, and had the person taken aspirin? [What about] the diabetic foot amputation that could have been prevented, and had the person checked their glucose and been taking insulin?

Even for a purely profit-maximizing insurer or health system, those are [interventions] you really need to get to disadvantaged people and prevent these expensive problems before they happen.

Health is special, because how do we use algorithms? Well, we can use algorithms to target sick people, and give them extra help and resources. Who do you want to find? It’s the most needy people who are going to get sick, and those people are the most disadvantaged people in our healthcare system.

Q. You mentioned at the beginning of this conversation that you’re feeling optimistic. What makes you feel hopeful about this field?

A. Through a lot of these collaborations with insurers or health systems, we’ve seen a lot of really great use cases of algorithms. I think algorithms can do good basically wherever human decision-making falls short.

If you’ve looked at the health system, you’ve no doubt seen at least one or two cases where humans don’t make the best decisions. I trained as a doctor; I still practice emergency medicine. And decision-making is just really hard in healthcare. It’s a complicated sector, with a lot of really hard things that humans have to do – complex data to process, whether it’s clinically or in population health or in insurance.

Anywhere that humans are faced with this super complicated set of data, and decisions that need to be grounded in those data, I think algorithms have a huge potential to help. We have this paper that shows that algorithms can really help a lot when we’re trying to figure out who to test in the ER for a heart attack.

There are lots of other population health management settings where algorithms can really help predict who’s going to get sick, rather than who just costs a lot of money.

So there are lots of cases where I think algorithms are really, really important, and they’re going to do a lot of good. That’s point one.

Point two is that we have to be really careful when we’re building those algorithms because very subtle-seeming technical choices can get you into a lot of trouble.

They can get you into a lot of trouble by doing harm to the people that you’re supposed to protect, but they can also get you into a lot of trouble with regulatory agencies and state law enforcement officials. It has not been a very good defense for organizations to say, “Oh, well, we don’t even have race in our algorithms or in our datasets, so we couldn’t be doing anything.” Ignorance is a very bad look in this area. That might be the most concrete message.

We’ve published this algorithmic bias playbook, meant for an audience of people exactly like forum attendees. It’s a step-by-step guide to thinking about how to deal with bias in algorithms that you’re using or thinking about using.

Starting to think about that organizationally, having someone responsible for strategic oversight of algorithms in your organization, having ways to quantify performance and bias in general – those things are really important for your mission and your strategic priorities. Algorithms are very powerful tools to help you achieve your goals, but also for staying on the right side of the law.

This interview has been condensed and lightly edited for clarity.

Obermeyer’s virtual panel with Ghassemi, Harris and Silverman, “AI Models, Bias and Inequity” is scheduled for 3 p.m. ET on Tuesday, Dec. 14.

Kat Jercich is senior editor of Healthcare IT News.
Twitter: @kjercich
Email: kjercich@himss.org
Healthcare IT News is a HIMSS Media publication.

Original Source: healthcareitnews.com

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Health Care

Singapore’s Public Health System Rolling Out the Clinician’s ZEDOC Platform

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Singapore’s health tech agency Integrated Health Information Systems has partnered with Auckland-headquartered digital health firm The Clinician to deploy a patient-reported outcome and experience measures platform across the island state’s public healthcare system.

WHAT IT’S FOR

The Clinician’s ZEDOC platform, the company describes, assists healthcare providers in managing patient-generated health data outside the hospital through digitisation. Integrated with HIS, the system supports timely exchange of health data and information between providers and patients, including subjective PROMs and PREMs, objective wearable device data, and other communication or educational materials. By streamlining the digital collection of critical health data, ZEDOC is able to render real-time, actionable information crucial for improving health outcomes and experiences.

The partners are working on multiple ZEDOC integrations with existing health information systems (HIS). A privacy-preserving hybrid infrastructure has been implemented which ensures that all personally identifiable information stays within the IHiS’s private health cloud while all anonymised health data are collected through a secure commercial cloud platform.

WHY IT MATTERS

Singapore intends to measure and improve health outcomes and patient experience with the rollout of The Clinician’s ZEDOC platform. Their partnership will “bolster patient engagement and enable clinicians to more effectively assess patients’ health status before, during and after receiving a health service – closing the loop when they are outside the hospital,” said The Clinician CEO Dr Ron Tenenbaum. It will also allow providers to deliver “more holistic and personalised care for patients by taking into account their perspectives for the first time,” he added.

To demonstrate the benefit of routine collection and analysis of PROMs, The Clinician shared that this has resulted in over 50% reduction in 90-day complications for hip and knee surgery patients in one study and a five-month improvement in the survival of cancer patients in another.

Among benefits for care providers, the ZEDOC integration will replace existing paper-based forms with an integrated digital platform that automates data capture, as well as benchmark outcomes across providers to reduce variability and waste. For patients, they can become more involved in the treatment decision-making and be informed early of health risks and warning signs.

THE LARGER TREND

Last month, Cabrini Health and The Alfred, two of the largest healthcare providers in the Australian state of Victoria, deployed the ZEDOC platform to automate the collection and analysis of health data from colorectal cancer patients. The installation is said to adhere to the colorectal cancer standards outlined by the International Consortium of Health Outcomes Measurement.

Original Post: healthcareitnews.com

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EU Analysis Highlights Digital Health Lessons From COVID-19

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An EU analysis has outlined the effect of COVID-19 on healthcare systems in Europe and the role of digital innovation in building their resilience.

Experts from the Organisation for Economic Co-operation and Development (OECD) and the European Observatory have published a set of 29 country health profiles, covering all EU member states, as well as Iceland and Norway. A companion report also highlights a selection of cross-country trends.

Speaking at a virtual launch event on Monday (13 December), Josep Figueras, director, European Observatory, highlighted two main lessons learnt from the use of technology in the pandemic.

Using telemedicine as an example of digital health innovation, he said the number of teleconsultations had increased in all EU countries during 2020. However in some countries, such as France, teleconsultations had decreased when lockdowns ended.

“The key issue here is how we harness and sustain innovation – how we make sure that these improvements in the use of telemedicine (as an illustration of the use of other digital technologies) can be maintained and sustained to increase the effectiveness of the health system,” Figueras said.

He also highlighted that the technology for telemedicine and other innovations was already available in many European countries before the pandemic but was not being used.

Figueras asked: “What did we do within the pandemic that literally within a couple of weeks, we got all this telemedicine in place?”

To sustain the use of telemedicine and other health technologies, he said it was important to look at the regulatory measures, financial incentives, training and changes in culture needed.

“Something the pandemic has taught us loudly and clearly is the importance of digital innovation – not only the new technologies, but the ability to implement them,” Figueras added.

WHY IT MATTERS

The State of Health in the EU cycle is a two-year process initiated by the European Commission in 2016, designed to improve country-specific and EU-wide knowledge in healthcare.

It aims to gather data and in-depth analyses on health systems and make the information accessible to policy makers and stakeholders.

THE LARGER CONTEXT

During the pandemic, digital tools have been used in the EU to boost public health measures such as the implementation of the EU Digital COVID Certificate, vaccination booking systems, and cross-border interoperability for contact-tracing apps.

There has also been investment in EU-wide COVID recovery initiatives such as the EU4Health programme.

ON THE RECORD

Maya Matthews, head of unit performance, European Commission said: “COVID-19 illuminated the fact that in many European countries we do not have a strong public health system. We cannot do testing and tracing. Even surveillance is done sometimes in a very fragmented fashion.

“I think if one thing comes out of COVID-19, it’s to say that public health matters – that public health is a very important part of health systems and has not really received the attention it deserves.”

Source Here: healthcareitnews.com

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Health Care

Clinical Messaging Platform Hospify to Close, Bupa Arabia Invests in Global Ventures, and More News Briefs

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Clinical messaging platform Hospify to close

British healthtech startup Hospify has announced it will close its secure clinical messaging platform on 31 January 2021.

Hospify said it suffered a decline in demand after the government suspended the UK 2018 Data Protection Act in relation to healthcare last year for the duration of the COVID-19 pandemic.

It also cited difficulties caused by “post-Brexit uncertainties surrounding the future of the UK’s data adequacy agreement with the EU”.

A statement from the Hospify team says: “It’s a sad end to a wonderful vision, a vision of universal health care communication that was both free of data exploitation and free at the point of use.”

Insurance giant Bupa Arabia invests in Global Ventures

UAE-based international venture capital firm Global Ventures has announced new investment from Bupa Arabia, the leading health insurance company in the region.

Bupa Arabia’s participation in Global Ventures Fund II as strategic partner aims to foster the healthcare ecosystem in the region and particularly in Saudi Arabia.

The investment is part of the Bupa Arabia’s strategy to participate and invest in disruptive healthcare and insurance technologies, amongst other targeted growth sectors.

Noor Sweid, Global Ventures founder and general partner, said: “Bupa Arabia shares our outlook and ambition on the digital health sector, and its potential for technology and innovation to deliver long-term economic benefits particularly in emerging markets.”

Liverpool Heart and Chest Hospital achieves EMRAM Stage 6

Specialist NHS trust Liverpool Heart and Chest Hospital (LHCH) has been awarded Stage 6 of the EMRAM, or Electronic Medical Record Adoption Model, by HIMSS.

The EMRAM measures the adoption and maturity of a health facility’s inpatient EMR capabilities from 0 to 7. Achieving Stage 6 means the trust has established clear goals for improving safety, minimising errors, and recognising the importance of healthcare IT.

Kate Warriner, chief digital and information officer said: “Digital excellence must be the cornerstone if we are to continually improve the care that we provide for our patients in the years ahead. Therefore, whilst we are rightly proud of this achievement, we have ambitions for further pioneering innovation and advancing our use of technology to become a Stage 7 hospital.”

More than $110m raised by Sheba’s ARC Innovation Center

Israel’s Sheba Medical Center has announced that six companies from its Accelerate Redesign Collaborate (ARC) Innovation Center raised more than $110 million (EUR97.2m) in 2021.

ARC brings new technologies into the hospital and community ecosystem focusing on digital health technologies including precision medicine, big data, artificial intelligence (AI), predictive analytics, telemedicine and mobile health.

Sheba MedTech startups receiving investments this year included: Aidoc, BELKIN Laser, Starget Pharma Append Medical, Innovalve Bio Medical and TechsoMed.

Professor Eyal Zimlichman, ARC director and founder, said: “The ARC Innovation Center has been focusing on ground-breaking, innovative technologies with a prime directive to redesign healthcare.”

Konica Minolta named as part of NHS Digital Documents Solutions framework

Konica Minolta Business Solutions (UK) Ltd has been named as one of 46 suppliers on the new ?5 billion Digital Documents Solutions framework.

The firm will provide solutions across five key areas: internal print, external print, digital mail room, scanning and electronic document management solutions.

Jason Barnes, head of public sector, Konica Minolta, said: “Having been chosen through a competitive tender process, we are especially pleased to be newly appointed to the LPP framework, which deepens and furthers our reach into the NHS health sector.”

Original Source: healthcareitnews.com

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