Can AI Address Health Care’s Red-Tape Problem?

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Productivity in the United States’ health care industry is declining — and has been ever since World War II. As the cost of treating patients continues to rise, life expectancy in America is beginning to fall. But there is mounting evidence that artificial intelligence (AI) can reverse the downward spiral in productivity by automating the system’s labyrinth of labor-intensive, inefficient administrative tasks, many of which have little to do with treating patients.

Administrative and operational inefficiencies account for nearly one third of the U.S. health care system’s $3 trillion in annual costs. Labor is the industry’s single largest operating expense, with six out of every 10 people who work in health care never interacting with patients. Even those who do can spend as little as 27% of their time working directly with patients. The rest is spent in front of computers, performing administrative tasks.

Using AI-powered tools capable of processing vast amounts of data and making real-time recommendations, some hospitals and insurers are discovering that they can reduce administrative hours, especially in the areas of regulatory documentation and fraudulent claims. This allows health care employees to devote more of their time to patients and focus on meeting their needs more efficiently.

To be sure, as we’ve seen with the adoption of electronic health records (EHR), the health care industry has a track record of dragging its feet when it comes to adopting new technologies — and for failing to maximize efficiency gains from new technologies. It was among the last industries to accept the need to digitize, and by and large has designed digital systems that doctors and medical staff dislike, contributing to warnings about burnout in the industry.

Adopting AI, however, doesn’t require the Herculean effort electronic health records (EHRs) did. Where EHRs required billions of dollars in investment and multi-year commitments from health systems, AI is more about targeted solutions. It involves productivity improvements made in increments by individual organizations without the prerequisite collaboration and standardization across health care players required with EHR adoption.

Indeed, AI solutions dealing with cost-cutting and reducing bureaucracy — where AI could have the biggest impact on productivity — are already producing the kind of internal gains that suggest much more is possible in health care players’ back offices. In most cases, these are experiments launched by individual hospitals or insurers.

Here, we analyze three ways AI is chipping away at mundane, administrative tasks at various health care providers and achieving new efficiencies.

Faster Hospital Bed Assignments

Quickly assigning patients to beds is critical to both the patients’ recovery and the financial health of hospitals. Large hospitals typically employ teams of 50 or more bed managers who spend the bulk of their day making calls and sending faxes to various departments vying for their share of the beds available. This job is made more complex by the unique requirements of each patient and the timing of incoming bed requests, so it’s not always a case of not enough beds but rather not enough of the right type at the right time.

Enter AI with the capability to help hospitals more accurately anticipate demand for beds and assign them more efficiently. For instance, by combining bed availability data and patient clinical data with projected future bed requests, an AI-powered control center at Johns Hopkins Hospital has been able to foresee bottlenecks and suggest corrective actions to avoid them, sometimes days in advance.

As a result, since the hospital introduced its new system two years ago, Johns Hopkins can assign beds 30% faster. This has reduced the need to keep surgery patients in recovery rooms longer than necessary by 80% and cut the wait time for beds for incoming emergency room patients by 20%. The new efficiencies also permitted Hopkins to accept 60% more transfer patients from other hospitals.

All of these improvements mean more hospital revenue. Hopkins’s success has prompted Humber River Hospital in Toronto and Tampa General Hospital in Florida to create their own AI-powered control centers as well.

Easier and Improved Documentation

Rapid collection, analysis and validation of health records is another place where AI has begun to make a difference. Health care providers typically spend nearly $39 billion every year to ensure that their electronic health records comply with about 600 federal guidelines. Hospitals assign about 60 people to this task on average, one quarter of whom are doctors and nurses.

This calculus changes when providers use an AI-powered tool developed in cooperation with electronic health record vendor Cerner Corporation. Embedded in physicians’ workflow, the AI tool created by Nuance Communications offers real-time suggestions to doctors on how to comply with federal guidelines by analyzing both patient clinical data and administrative data.

By following the AI tool’s recommendations, some health care providers have cut the time spent on documentation by up to 45% while simultaneously making their records 36% more compliant.

Automated Fraud Detection

Fraud, waste, and abuse also continues to be a consistent drain. Despite an army of claims investigators, it annually costs the industry as much as $200 billion.

While AI won’t eliminate those problems, it does help insurers better identify the claims that investigators should review — in many cases, even before they are paid — to more efficiently reduce the number of suspect claims making it through the system. For example, startup Fraudscope has already saved insurers more than $1 billion by using machine learning algorithms to identify potentially fraudulent claims and alert investigators prior to payment. Its AI system also prioritizes the claims that will yield the most savings, ensuring that time and resources are used where they will have the greatest impact.

Getting Ready for AI

When it comes to cutting health care’s administrative burden through AI, we are only beginning to scratch the surface. But the industry’s ability to amplify that impact will be constrained unless it moves to remove certain impediments.

First, healthcare organizations must simplify and standardize data and processes before AI algorithms can work with them. For example, efficiently finding available hospital beds can’t happen unless all departments define bed space in the same terms.

Second, health care providers will have to break down the barriers that usually exist between customized and conflicting information technology systems in different departments. AI can only automate the transfer of patients from operating rooms to intensive care units (ICU) if both departments’ IT systems are able to communicate with each other.

Finally, the industry’s productivity will not improve as long as too many health care personnel continue in jobs that don’t add value to the business by improving outcomes. Health care players need to begin reducing their workforces by taking advantage of the industry’s 20% attrition rate and automating tasks, rather than filling positions on autopilot.

The task of improving productivity in health care by automating administrative tasks with AI will not be completed quickly or easily. But the progress already achieved by AI solutions is encouraging enough for some to wonder whether re-investing savings from it might also ultimately cut the overall cost of health care as well as improve its quality. For an industry known for its glacial approach to change, AI offers more than a little light at the end of a long tunnel.

Source: Harvard Business Review

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Come and say hello

Tech Summit

NYU is hosting the inaugural Technology Summit to celebrate and showcase innovative and emerging technologies used in teaching, learning, research, administration, and entrepreneurial efforts in tech, and beyond.

I’ll be giving a keynote on AI in Healthcare. Come and say hi on 11/14 at Kimmel Center for University Life, 60 Washington Square S., New York, NY 10012

Top 5 tips for businesses implementing RPA

To remain competitive, businesses must digitalise their operations. One way to speed up this process and improve returns is through robotic process automation

Robotic process automation (RPA) is gaining popularity as enterprises discover new ways to drive business impact and speed up digital transformation. RPA is software that mimics how humans use applications to process transactions, harness data and communicate with other systems.
RPA can provide businesses with fast returns on investment by automating manual data processes, freeing up employees for more value-added tasks and improving operational and cost efficiencies. For enterprises that are digitally transforming their operations, RPA software is becoming fundamental to improving productivity, compliance and competitive advantage. The new economy outlines the top five tips businesses should consider for successful RPA implementation:

Start small and learn
By starting their digitalisation transformation with RPA, enterprises can more effectively plan how they will tackle the process. For example, some businesses may look to identify and automate the lengthiest and most repetitive tasks first, before replicating this in other processes. Others may consider automating operations that can impact a specific function.
RPA is at its most effective when combined with other technologies, but starting small and automating simple, tedious and repetitive processes is a good way to develop a strategy that can be implemented more broadly later on.

Implement holistically
Robotics not only enables companies to automate time consuming human processes, it also promotes innovation in terms of how the business is run and what services are offered to customers. For this reason, senior executives should approach RPA implementation holistically, keeping end-to-end processes in mind and looking for opportunities to exploit machine learning and analytics.
Robotics not only enables companies to automate time consuming human processes, it also promotes innovation
For example, an employee might manually open an email and a PDF attachment, review an invoice and enter that information into a software system. RPA streamlines this process, entering invoice amounts into the system faster and more accurately. In doing so, the software creates actionable business insights that can be used to improve process performance even further.

Consider your workforce
Traditionally, business process operations are intensive, service level-focused environments dealing with planned and unplanned peaks, seasonal variations and exhaustive end-month and end-week periods.
When RPA is introduced, this working environment changes dramatically. Automated processes not only work faster than their human counterparts but they can work 24/7 and automatically scale up to deal with peak demand and scale down in periods of lower intensity. There needs to be a shift away from traditional workforce management and rostering, towards a more skilled, customer-focused, value-adding workforce.

Train bots like humans
Treating software bots as human staff is not as farfetched as it sounds. To achieve the most with RPA, businesses should first give their bots something small and task-orientated to work on. Throughout the bots’ lifecycle, it is important to carry out maintenance and reviews, just as you would with human staff evaluations.
Enterprises should make bots just as accountable to the business as the human workforce is. The most successful RPA implementers have maintained this clarity of accountability.

Establish automation governance
Enterprises need to establish automation governance systems as an extension of corporate governance. To ensure robotic processes comply with regulatory controls, businesses must closely monitor and manage digital workers. Businesses must also ensure they have a clear understanding of what robotics laws are in place and how to effectively comply with them.
RPA is an important asset in any enterprise’s digital transformation. By implementing automation with time-intensive, manual, administrative tasks first, companies can learn from RPA-enabled processes and replicate those successes elsewhere in the business. Combining RPA with a broader set of technological tools ultimately improves business outcomes across end-to-end processes.

Source: theneweconomy.com

7 Robotic Process Automation Pitfalls & How to Avoid Them

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In order to implement RPA “the smart way” and take the most advantage of it, you should be aware of the robotic process automation pitfalls from the very beginning.

We must all acknowledge and take seriously the fallibility of our endeavours, and, consequently, we should not allow ourselves to indulge in surreal expectations. The simple truth is that sometimes projects fail, for a very large number of reasons. According to IBM Systems Magazine, up to 25% of technological projects fail downright, while up to half of them require extensive revisions by the time they are set to go.

Bernard Marr writes in Forbes adds that more than half of the technological failures are in fact due to poor management, and only 3% are caused by technical problems. DIfficulties may also arise from not choosing the right processes to automate.Here are 8 questions to ask that should ease decision making in this regard.

So let us now delve a little deeper into potential robotic process automation pitfalls and corresponding means to avoid them in the course of implementing RPA.

Forethought is definitely needed for good results and successful RPA implementation. Of course you should first spell out what ‘successful’ means; but for now, let us tackle the question about what could go wrong during the implementation of your software robots. Here is a list of 7 aspects that ought to be considered and/or avoided if you want to stay safe from robotic process automation pitfalls.

1. Not choosing the right processes to automate in the beginning

This refers to picking the process that is most appropriate for an effective start of using automation in your business. By no means should you neglect a thorough, exhaustive and, of course, realistic evaluation of the tasks that may be passed on to robots. You do not want to start automating the wrong things, resulting in difficult to manage workflows.

A piece of advice courtesy of Cem Dilmegani, CEO at appliedAI, is that you should consider features like, for example, the process complexity and its business impact. Briefly put, you should perform a cost – benefit analysis of automating the candidate processes, based on what you consider to be your top goals.

2. Trying to implement robotic process automation on your own

You probably know this by now, otherwise you wouldn’t be reading this: RPA provides highly technical ways to carry out faster and more efficiently the dull jobs that would cause your employees unnecessary distress, boredom and fatigue. Precisely because of the high level of technicality, it is not at all advisable that you attempt to carry out the implementation process on your own.

Division of labour is with us for good reasons, so you must not forget to delegate the responsibility of implementation to the specialists who can best handle it. Tony Warren, executive vice president, head of strategy and solutions management at FIS, mentions things like “technical maintenance, operational monitoring and the appropriate change management procedures” among the RPA features that call for the right level of expertise, which specialist implementation navigators possess.

3. Not setting clear objectives for your automation strategy

This is a more general rule of thumb: it is vital that your business objectives, as well as the role that you expect RPA to play in getting there, are crystal clear.

What do you need RPA for?

Relatedly, which software provider is likely to do the best job for what you need?

While uncertainties in these respects are likely to be burdensome, definite answers to such questions will facilitate a smooth transition to delegating the tedious, repetitive tasks in your business to software robots.

4. Not having a “bird’s eye view” over the implementation process

As you probably know by now, RPA implementation is a complex enterprise. In fact, this comes as no surprise for an activity meant to take such deep effect on your business. So in order to achieve your goals, you need to ensure proper executive control.

This requires a group or an individual who can watch over the whole process from the top, so to say. Some call this essential aspect “operational oversight”, others – “governance of accretion” or simply “governance”, while others emphasize how important it is to include in the responsible team not only domain-specific specialists but also someone to take over the executive role of “central process unit”. In the long run, this can take the form of a robotic process automation centre of excellence that warrants a strategic maintenance of the system.

5. Not ensuring the scalability potential of your software robots

Scalability is one hidden gem that is certainly responsible for the larger-scale adoption of RPA. Which means that you really should not allow anything to stand in the way of scalable bots that can ensure consistent, across-the-board use of RPA in individual departments of your business.

6. Relying solely on the IT department

You certainly do not want to condition the smooth running of your automated processes to the IT department. Of course, it goes without saying that IT assistance is necessary for automation, but the idea is that you should not overdo it.

The bottom line is something along the lines of the phrase ‘render unto IT the things that are intrinsically IT-related (e.g. automation codes), and unto other departments the things that are better dealt with by other departments’. As Schultz puts it, “finance cannot depend on IT for RPA; it needs to be owned by the business side.”

7. Not testing your software robots thoroughly

Even if you may not like the phrase ‘haste makes waste’ after having heard it one million times, you have to admit there is some truth to it. And since you do not want to waste the effort, time, money and hope that you invested in RPA, you also do not want to stumble at the threshold.

As our own Daniel Pullen puts it, you need to test processes in production prior to full go-live to ensure there is a like-for-like behaviour between Dev and Production. This includes ensuring the applications are the same version, testing applications under normal and peak loads throughout the day, servers & applications in a server farm all behave identically (both operation and speed), etc.

Conclusion

We believe that you are now better prepared to embark on a successful RPA journey. Failure anticipation is not meant to alarm you, rather to motivate you to have a realistic view over what might happen so that you can prevent the pitfalls.

Anticipating and planning pro-actively should take you a step closer to gloriously passing the finish line. Although the word ‘finish’ is not perfectly fit here, since what you aim for with robotic process automation is a long-term sustainable development of your enterprise. As UiPath puts it, with “a comprehensive understanding of your company’s automation needs and the value proposition RPA provides, you can ensure a successful RPA implementation scheme that is both cost-effective and timely”.

Such extensive understanding can lead you to make use of the best practices for robotic process automation implementation. Wisely selecting the processes, a plain understanding of the required human resources or reliance on an ‘RPA sponsor’ are some of those practices, on which you can read more here.

Source: cigen.com.au