The integration of Artificial Intelligence (AI), automation, and data analytics into the revenue cycle management (RCM) of healthcare facilities marks a transformative leap toward operational excellence. In an era where financial sustainability is as crucial as clinical outcomes, these technologies are pivotal in optimizing processes from patient intake to final billing, ensuring that healthcare providers not only survive but thrive in a competitive market.

At our infectious disease lab, the implementation of AI and data analytics has revolutionized how we manage our revenue cycle. By automating routine tasks, we have freed up valuable time for our staff to focus on more complex, value-added activities. Automation of data entry and claims processing reduces the likelihood of errors and speeds up the turnaround time, directly impacting our cash flow and reducing the days in accounts receivable.

One of the most significant advantages of using AI is its ability to analyze vast amounts of data to identify trends and patterns that would be impossible for a human to discern. This capability allows us to anticipate issues before they become problematic, such as identifying which claims are likely to be denied based on historical data. With predictive analytics, we are proactive rather than reactive, which not only increases our revenue but also reduces the stress on our staff and improves our relationships with patients and insurers.

AI transforms data into actionable insights, enhancing efficiency and profitability in healthcare.

Moreover, machine learning (ML) models within our AI systems continuously learn from new data. As they become more sophisticated, they offer increasingly accurate forecasts and deeper insights into our lab’s financial operations. This ongoing learning process is crucial for adapting to the ever-changing landscape of healthcare regulations and insurance policies.

Our organization has also capitalized on data analytics to fine-tune our pricing strategies and to ensure compliance with billing regulations. By analyzing the outcomes of thousands of past transactions, we can set competitive prices that maximize our revenue without compromising patient care. Furthermore, compliance monitoring through AI-driven systems ensures we adhere to all billing regulations, reducing the risk of costly penalties and legal issues.

The integration of these technologies extends beyond internal operations to enhance patient interactions. Our patient portal, powered by AI, offers personalized experiences where patients can easily access their billing information, understand their payment options, and communicate with billing representatives seamlessly. This not only improves patient satisfaction but also expedites payments, positively affecting our cash flow.

In addition to these operational improvements, AI and data analytics significantly enhance our strategic decision-making capabilities. With access to real-time data and advanced analytical tools, our management team can make informed decisions quickly, addressing potential financial discrepancies and optimizing overall financial health.

Furthermore, the ability of AI to integrate with other technological advancements, such as electronic health records (EHRs), further streamlines our operations. This integration ensures that all patient data is synchronized across platforms, minimizing the risk of data silos, and ensuring that every department has access to the same accurate and updated information. This seamless integration helps in maintaining consistency in billing practices and patient care services.

Our commitment to leveraging AI extends to training our staff to effectively utilize these tools. By holding regular training sessions and workshops, we ensure that our team is not only comfortable but also proficient in using the latest technologies. This empowerment enables them to contribute actively to our ongoing efforts to refine and improve our revenue cycle processes.

Additionally, AI tools help us manage the complexities of insurance verification and eligibility checks with greater accuracy. By automating these processes, we reduce the instances of claim rejections due to coverage errors. This not only speeds up the billing process but also decreases the burden on our patients, who can be confident that their coverage is correctly verified at the outset of their healthcare journey.

Moreover, AI-driven analytics assist us in identifying inefficiencies in our billing and service delivery models, allowing us to make necessary adjustments. These adjustments are often predictive rather than reactive, positioning us to address potential issues before impacting our operations. This foresight saves time and resources and supports our strategic goals of maintaining financial health and patient satisfaction.

The adoption and continual advancement of these technologies in our revenue cycle processes illustrate a commitment to innovation and excellence in healthcare management. As these tools evolve, so too does our ability to meet the needs of the patients we serve and the staff we support, ensuring a future where healthcare and technology work hand in hand for the betterment of all involved. As we continue to harness these powerful technologies, we not only foresee a more robust financial footing for our lab but also a greater capacity to provide exceptional care to our patients.

Through ongoing investments in AI and data analytics, we not only optimize our current operations but also pave the way for future innovations. These technologies allow us to stay at the forefront of the healthcare industry, continually improving our services and outcomes. By embracing AI and automation, we not only enhance our operational efficiencies but also ensure a higher standard of care, which is the cornerstone of our mission in healthcare.

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As health systems navigate their way through an unparalleled age of technological advancement, CIOs can tap into an overwhelming number of state-of-the-art solutions capable of driving their digital transformation strategies forward. From wearables to mobile health devices, augmented reality to machine learning, the choices are seemingly endless.

And while all this technology is captivating, it also can be blinding. It’s too easy for health systems to let technology run the business of health care. Instead, it’s the business of health care that must drive the technology, because digital transformation will only make a real impact on a health system if it ultimately helps to improve care delivery and patient outcomes.

We’re fortunate at Atlantic Health System to have innovative, diverse leadership and a team of health care professionals covering a span of generations—baby boomers, Gen Z’ers, millennials and Gen X’ers—that embody this philosophy. Working together, we tackle digital transformation with a single question: What is the problem we’re trying to solve? Many times, the answers come from the clinical side rather than the technical side.

This methodical, collaborative approach gives us a system of checks and balances that ensures the technologies we invest in will help us achieve a specific ROI. It also allows us to make technological enhancements that augment—and not replace—the care our health care professionals deliver. We always include the human element in our workflows to strengthen the patient-clinician bond.

Over the past 12 – 18 months, we’ve seen encouraging results from two particular technologies: AI and Big Data. In both cases, the primary measurement is how well these solutions can help health care professionals treat disease sooner, creating better health throughout the communities we serve.

How AI enhances Medical Imaging

As Atlantic Health System grows and cares for more people, delivering highly sophisticated medical imaging takes high priority. As we searched for optimal imaging solutions, we prioritized technology that could help our radiologists and health care professionals get more efficient, find abnormalities faster and contact patients sooner for follow-up tests and appointments that can save lives.

To achieve these goals, we implemented three highly integrated solutions. The first, a modern picture archive and communication system (PACS), streamlines the entire radiology workflow.

By integrating our PACS solution with our Epic EHR, our radiologists can now access patient information and images from a single cloud-based system with enterprise-grade security that offers enhanced protection from ransomware and other malicious attacks. Our clinicians report high satisfaction with the quality of images on our new PACS system.

The second tool in our radiology arsenal is a FDA-approved decision support software solution. It uses AI to scan large volumes of images (such as CT scans), flag images that contain abnormalities and move them to the top of a radiologist’s or health care professional’s to-do list.

While the technology flags suspected acute pathologies, the human element is the key factor in this workflow. That’s because it’s the clinician who reviews the flagged images, identifies potential life-threatening anomalies—intracranial hemorrhage, acute spinal fractures, pulmonary emboli—and expedites patient care so patients with the most acute needs get seen right away.

Rounding out our medical imaging technology cycle is a radiology report management solution. It uses AI and NLP to comb through clinical notes and imaging scans. It then notifies the care team if and when patients need to follow up. This helps our health care professionals close the loop with patients faster and find potential diseases earlier.

We used EDAP data to identify potential COVID-19 hot spots and quickly ramp up supplies, staffing and resources in facilities that served those communities.

Viewing Big Data from an enterprise-level

Most businesses today are ingesting more data than ever before, and health care is no exception. However, most health care organizations have data spread across multiple legacy systems or locked in department-specific silos, which reduces the ability to act on that data. We faced the same challenge of bringing data together so we could make optimal business decisions.

Our answer: building an Enterprise Data and Analytics Platform (EDAP). The EDAP solution gathers data from 63 different sources, including our Epic EHR, claims data, health quality data, financial data and more. EDAP allows us to ingest, curate, create and model data, giving us a robust data pipeline capable of creating predictive and prescriptive models.

In addition to investing in EDAP, we’ve recruited highly skilled data scientists and aligned them with each vertical inside our health system. The expertise of our data analysts, combined with EDAP technology, allows us to identify operational, financial and clinical efficiencies and ultimately improve patient care.

One real-world example of how EDAP benefits patient care came during the COVID-19 pandemic. We used EDAP data to identify potential COVID-19 hot spots and quickly ramp up supplies, staffing and resources in facilities that served those communities.

Fueling adoption of innovative technologies

Implementing emerging AI-powered technologies and other modern solutions is just one part of the battle. The second is fully adopting them. At Atlantic Health System, we incorporate both e-learning and in-person instruction to make sure all health care professionals and users know how to use the technology, understand the workflow and interpret the data they’re reviewing, fueling widespread adoption and competency.

And while AI and Big Data are showing the best results for us right now, we’re also introducing many other emerging platforms—from customer experience technology to machine-learning-driven solutions and even augmented reality. Each is at a different maturity level. By looking at these solutions through the lens of problem-solving, then taking a pragmatic approach to implementation and training, we’ll keep developing innovations that help us expand the number of people we can help in our communities and enhance the care we provide them.

The post Using AI and Big Data to Improve Medical Imaging and Care Outcomes appeared first on HealthTech Magazines.

Artificial Intelligence (AI) is now playing a significant role in the applications we use every day or the websites we go to. YouTube, Facebook, and even Google use some form of artificial intelligence. AI offers great benefits to improve the technology we use and at the same time displays unintended uses or results that can lead to problems, such as ethical issues.

Before going into the bad, it’s good to understand why AI is used in the first place. If AI can cause problems, then why even use it? Artificial intelligence is almost necessary for today’s technological advances. It’s harder to improve how specific technical applications and platforms run and return results without using some form of AI to help us. AI is a learning algorithm that can help us understand current information and predict future information searched or needed. 

Let’s consider the example of Google. Google uses an algorithm to show ads based on previous search criteria. This can be in the form of not only company websites thatusers have visited, but it may be items that were merely searched for. For example, we put in the Google search bar “scrubs.” An array of companies, online stores, and pictures will be displayed to you. It probably won’t end there because the next time you return to Google, you may have ads for scrubs appear, even if you searched for something completely different. The same could be said of YouTube, where ads for scrubs may pop up during a video.

It is best to have a diverse team when creating AI algorithms and keep the project guidelines and group remembering three words while creating and launching the algorithm: fair, accountable, and transparent.

Given these examples, AI integration is seen to be very positive for various businesses that thrive on online searching and shopping. However, not everything is perfect, and unfortunately, unintended and even unintended negative consequences can come out of AI usage.

An example of a largely negative ramification from errors in an AI system happened in Arkansas. Buggy software caused health benefits to be altered for hundreds of people, including many with severe disabilities. One example was a woman named Tammy Dobbs with cerebral palsy who needed an aide to do daily tasks around the home but suddenly lost several hours a week to receive an aide because of the previously mentioned buggy AI system. Blame was passed around between the one who developed the AI and the government, more explicitly blaming government policies. This example shows the type of issues that can arise with AI when the implementation is buggy, and how the use of AI can make it easier not to feel responsible.

That is just one example of how not adequately written or monitored AI can be skewed. The example above listed a problem that negatively affected a group of people. It will not always be harmful in that way, but AI errors can lead to ethical issues. What causes these types of ethical issues in AI? One piece of it is how the algorithm was written. If the team who wrote the algorithm was not very diverse, they will most likely all miss the same problems in the algorithm that may be easily seen from someone with a different ethnicity or background.

An example of this could be adding a block button to a new social networking application. Suppose any of the staff creating the application never had adverse consequences from previous social networking apps that needed this option. In that case, they may not even think of it, versus having one or a few people on the team who have had that problem and believe that is an excellent option to have for those who want to use the social network safely and securely.

It is hard to cover the whole topic of ethics in AI, as the subject is just too vast to cover fully, but hopefully, some of these points help give some food for thought as we dive more into healthcare.

Healthcare stands for a wide range of businesses, from hospitals to private practices, to specialized doctor’s offices, and so on. AI is considered a more behind-the-screen type, so how can AI help places like these work more with the public? The answer to that is quite simple. Healthcare often has computer systems to save patient information, from basic information to their diagnosis and the treatment decision. AI can be used to keep track of this information. It can also be used to understand better the number of supplies and the equipment that is needed based on the number of cases of various illnesses, such as flu numbers or patients with diabetes, from month to month, or even year to year.

AI is a beautiful tool, but just as in the examples presented earlier, ethical issues can arise. Assuming diagnoses based on age and gender, for instance, is a common ethical problem. If an older man comes in with chest pain, it might be assumed that he could be having a heart attack, but it only ends up being a solid case of heartburn from the burger he ate earlier that day.

This may seem like a silly example, but ethical considerations need to be seriously thought through; such significant and catastrophic implications don’t negatively affect innocent people on both narrow and wide scales, such as the example given with Arkansas. It is best to have a diverse team when creating AI algorithms and keep the project guidelines and group remembering three words while creating and launching the algorithm: fair, accountable, and transparent. If these ideas can be maintained and a comprehensive, diverse team is put in place, the creation and monitoring of ethical AI may improve, with fewer issues during usage.

The post Flawed Humans Create Flawed AI, Even in Healthcare appeared first on HealthTech Magazines.

There have been spectacular advancements in data insights and machine learning capabilities in the last ten years—all driven by cloud economics, availability of large quantities of data, and massive investments. While we’ve seen certain industries taking advantage of these advancements, others, including healthcare, have far more opportunities than they do successes in the machine learning space.

So, where are we today in healthcare?

We are exponentially increasing the amount of data being generated:

1. Firstly, we are putting sensors into and onto seemingly everything: houses, buildings, streets, cars, planes. Not to mention people.
   
   
2. Secondly, sensors are getting increasingly sophisticated and intelligent, which means they can tell us a lot more than they used to.

While some data is just noise, a lot of it is very valuable on its own and even more so in aggregation to other data from different sources. 

Not only do we have the data we need, we now have significant needs to utilize machine learning to drive enormous value in the healthcare space: 

1. Healthcare operations must become significantly more efficient, reducing costs without reducing the quality of care. Efficient staff and physical resource scheduling immediately comes to mind in the clinical space (with the added bonus of positive outcomes to patients), while efficient supply chain management rises to the top in the administrative operations space. This is something other industries, such as financial services, hospitality, travel, and entertainment parks, have done successfully for some years.
   
   
2. The need for diagnostics support using narrow AI has been increasing exponentially in the last 2-3 decades. Expansion in sophistication and prevalence of tests, deeper understanding of complex medical conditions as well as compounding interactions of many factors, including medications, means there is more and faster way to analyze now than ever before. For narrow applications, AI has proven it can help. While notable commercial applications providing relatively high accuracy exist and new ones appear constantly, the road to an AI-rich environment in this space is still long. Commercial vendors and startups working with physicians and the FDA and supported by vast amounts of VC capital will ultimately fulfill the need.
   
   
3. Finally, systems assisting physicians, including by minimizing the administrative EHR burden, would be very valuable. Moreover, systems actively assisting during physician activities (consult or even surgery) and either providing recommendations, summaries, updates, notifications, or alerts in real-time based on the situational context would be priceless.  And, increasingly, this will mean the use of what we would call real-time intelligent assistants.

It is the intelligent assistants where I think we will see a tremendous amount of value. This is where humans and computer “smart” systems would actively and dynamically be collaborating in real-time on relatively sophisticated tasks.

It is the intelligent assistants where I think we will see a tremendous amount of value. This is where humans and computer “smart” systems would actively and dynamically be collaborating in real-time on relatively sophisticated tasks. The systems would not merely respond to requests or input from the human but proactively, independently, and dynamically process the actual task context, actions, and current flow state, efficiently collaborating with humans.

This capability will be powered mainly by comprehensively injecting real-time Machine Learning insights in all existing applications, and creating specialized capabilities that don’t exist today.

Active human-system collaboration is already happening in some basic, generic systems. We are seeing the start of it in more complicated systems and we will progressively see it built into other commercial systems of increased sophistication. As developers and vendors of digital capabilities, we must actively pursue the injection of ML insights into all those cases where it makes sense to enable the system to “collaborate” with humans.

At the end of the day, through, for such intelligent assistants to see adoption, the systems would have to prove effectiveness and accuracy. If, for example, a surgery assistant gets in the way during an operation instead of helping, you wouldn’t use that person next time. Similarly, a system’s incorrect activity could lead to very adverse outcomes, so it would not be used. Therefore, the feature must work very well and be adequate to the time/place/action before making it comprehensive. Less but very good is better than more but buggy.

Despite all these exciting opportunities, so far, a significant size of advancements in applied ML have been happening mainly in distinct but limited areas, such as marketing personalization, and mostly by technology companies, startups, financial and streaming giants. Unfortunately, everybody else, including in healthcare, has done a lot less than they could and should. Too much talk and too little walk in too many quarters, as they say.

How do we change that?

First of all, from a technical perspective, for AI/ML to be impactful, the predictions must get injected, preferably in real-time, into the operational systems. There are two parts to getting this benefit:

1. Exposing the recommendations coming out of ML so they can be easily consumed, preferably through APIs.
   
   
2. Changing the operational systems to consume this data. The insights must be consumed correctly and effectively at the point they add the value – either in transactional systems (which therefore must be enabled to consume the insights) or sometimes directly by humans. That part is more expensive, especially if those systems are not modern enough.

Secondly, we need to move our cultures and decision-making processes to be more data-driven versus opinion-driven. There are culture and change management aspects to be managed, as well as trust on insights (models explainability can go a long distance towards establishing that trust).  Sure, data can be interpreted differently or misinterpreted, but that does not mean we should manage out of opinions and gut.

Thirdly and finally, we must be deliberate and strategic in where we focus our data efforts:  

• Aligned with whatever strategic direction and goals we have (those are still very much important in an agile world)
  
  
• Where is the biggest challenge?
  
  
• Where is the biggest opportunity for positive impact?
  
  
• To support regulatory or compliance requirements 

Rigorous prioritization is critical, as trying to do everything at once just because everybody wants the outcomes of ML immediately only leads to diffused efforts that lead nowhere.

In conclusion, renewed and robust efforts to introduce AI and ML are something we must all do. And since we know how to do it by now, it is now a matter of will and execution.

The post Taking AI and Machine Learning to the Next Level in Healthcare appeared first on HealthTech Magazines.

How do you use data to find long-term chronic health problems?

Healthcare practitioners function in an event-based system with some discreet data points, but most of the good information about patients is contained in unstructured notes.

How can we use these notes to piece together chronic conditions and their causes across multiple visits, multiple doctors, across a sea of time?

Fortunately, many other industries have been down this path before and found ways to transform how they used data and technology to differentiate themselves. Healthcare, meet Big Data, AI, and the Digital Twin!

The glut of data, data everywhere

Each visit to a healthcare provider generates a ton of data about who you are, why you are visiting, what the diagnoses are, additional information needed in labs or images, and next steps. After the visit, even more information is generated through back-end billing information to the insurance provider.

Over the course of many different visits, to different providers, different systems, and over many years, the amount of data can create a complex, messy picture.

The retail transformation brought on by Amazon offers a roadmap for how healthcare might handle this glut of data. Amazon users know that the company creates an efficient and tailored experience based on what the user has looked at and purchased before. It’s almost like they knew what you wanted, and it engaged you in a way that you have now come to expect. This did not happen without using many individual points of data over time to get patterns and actions.

In healthcare, unlike in retail, much of the important data must be teased out of free-text notes. In order to get a accurate picture of a patient’s health, all of the relevant historical notes and images would have to be read and assimilated.

Where is the good stuff?

In healthcare, unlike in retail, much of the important data must be teased out of free-text notes. In order to get a accurate picture of a patient’s health, all of the relevant historical notes and images would have to be read and assimilated. This type of investigation is laborious for clinicians but also necessary to truly understand chronic diseases.

Let’s take a typical MS patient, for example. Flare-ups may manifest themselves over years, coming in loss of hearing for a week or two, losing eyesight for a brief period of time, or numbness in the legs that seems to come and go. The patient might go to an ear doctor, an eye doctor, and a spinal specialist. The individual specialists might not put together a bigger picture because of the nature of the interfaced systems, the dispersed data, and the amount of time needed to review all of the notes. Those notes could include innocuous-seeming but insightful clues: headache, fatigue, the environment at the time of the flare-up.

This example shows that much of a patient’s data is not captured in regular columns but is contained in free-text fields that, unless read or pulled out, are the missing key to creating a full picture of health.

Unlocking it into something I can use

Using Natural Language Processing and Machine Learning, or ‘AI’, against this sea of data is the key to distilling out useful bits of data to be a part of the complete picture. By adding these key data points to the visits through NLP, the ML can start to recognize patterns that are more meaningful to understanding the overall patient health profile.

The key is providing that information within an easy-to-consume and actionable manner that removes the friction of too much data and not enough information. At Novant Health, we implemented these processes in the electronic medical recordMeeting the clinicians where they spend their working time in the EMR means that all the data is put through the Big Data and AI machine to assist clinicians with investigation and treatment.

Making big progress and the challenges ahead

While implementing the NLP and ML is technically challenging, the bigger hurdle in our heathcare system and in others is AI’s acceptance. We’ve met that challenge by holding to explainable AI and no black boxes principles, and the technical teams have worked side-by-side with those who will use the new tools. That clinical user input and partnership help zero in on the differentiating bits of information and provide insights into what is needed next.

The next iteration of healthcare AI will be the development of a “digital twin,” a digital model of the patient created using the full range of collected data. Clinicians can use the digital twin, with all new compute power, to simulate different treatment factors.

In this next frontier of digital medicine, the digital twin will also provide new insights about how diet affects chronic diseases and will help narrow down food triggers without trial and error.

In this future of personal health, data will be a more powerful tool for clinicians and patients in managing disease because important information will no longer be left behind in clinician notes.

The post How Big Data and AI are helping sift through data appeared first on HealthTech Magazines.

One of the most significant changes facing healthcare providers today is digital transformation. One hospital system CEO described the digital transformation as having even more of a long-term impact than the COVID-19 pandemic. To succeed in this endeavor, healthcare providers need to ensure they are focusing on the right dimensions that integrate the entire ecosystem, incorporate the right technologies and plan appropriately to avoid potential pitfalls during the journey.  

The cloud is not merely someone else’s compute and storage, though that brings improved cost, provisioning, and management at a level of sophistication hard to replicate on-premise.

So, what does “digital transformation” mean exactly? It’s one of the most elusive of terms. It really means changing and transforming the business and operational models by using digital and data technologies (aka digital). So, while digital transformation does not mean just technology, technology is critical. 

While digital looks very easy from a distance, especially when done well, it requires relentless attention to detail on both the user experience and the back of house operations to create a seamless and useful digital experience. As such, digital initiatives for healthcare providers must focus on four experiential dimensions: 

• Consumers (pre- and post-visit) – think basic items like the ease of booking and changing appointments, and more complex solutions like remote health monitoring and personalized medicine.
  
  
• Patients (During clinical interactions) – including ease of check-in, digital experiences in hospitals and clinics, and telehealth options.
  
  
• Clinical Staff (Physicians and Nurses) – this includes digital tools and experiences to allow the staff to spend most of their time focusing on patients rather than administrative work.
  
  
• Business (Operations and administration) – centered on tools to make operations more efficient through automation, data analytics, and machine learning 

In order to seamlessly integrate these dimensions, innovative technologies are going to be game-changers. The adage that says you can’t solve tomorrow’s problems with the tools you used to create them is very applicable here. Three sets of technologies stand out: the cloud, machine learning (ML), and remote sensors. 

The cloud is not merely someone else’s compute and storage, though that brings improved cost, provisioning, and management at a level of sophistication hard to replicate on-premise. The cloud ecosystem offers benefits and solutions that can solve some of the health care industry’s greatest challenges.  

• Flexibility and speed to the market allow tenants to quickly innovate and tune solutions to meet consumer’s reactions, as well as sudden events such as the ones driven by the pandemic. When combined with agile development, cloud speed will go a long way towards solving healthcare IT’s perception as being slow and non-responsive to consumer needs. 
  
  
• The cloud offers an ecosystem that can be quickly incorporated into the systems’ own solutions – such as increasingly sophisticated “automatic” machine learning model selection. This allows for new technologies to be plugged in, helping solve issues that have existed for a while. 
  
  
• Not without its downside, migration from on-premise tends to be costly and comes with some risk, so strategically building the plan and ensuring readiness is critical. 

Machine learning-based predictive and prescriptive analytics have long held the promise of better outcomes in medical treatments. These analytics pave the way for efficiencies through optimization of operating rooms, clinicians, staff scheduling, and more personalized recommendations and care. Fortunately, the science and the technologies behind it are becoming widely available and relatively inexpensive, especially in the cloud.  

Tempering the promise of ML are a number of challenges: 

• Business users don’t fully understand and trust the value of ML recommendations. As such, a show-and-prove tactic is critical for adoption. 
  
  
• The old problem of data quality still exists, and data cleansing must continue to be a big focus. However, non-glorious may be. 
  
  
• “Injecting” the recommendation into all systems and applications is the bulk of the work and thus the cost as well.  
  
  
• On the positive side, the old fallacy of “you need lots of PhDs to do data science” has mostly been debunked by reality. What is needed are people who understand how to use the widely available models and the still too complex technology stack.  

Finally, the remote video doctor-patient interactions that skyrocketed in number during the pandemic is just one sign of the upcoming remote and home care trend, mostly based on real-time sensors and sensor management platforms. True health care doesn’t only happen when you talk to a doctor. And while IoT and at-home sensors are still not as robust as we’d like, the biggest challenge to the transformation is not technology, but legacy business, incentives, and operational models. 

Cloud, Machine Learning, and IoT technologies are not just topics for the CIO to talk about, but true business capabilities that the CEO must understand and support to fully realize the benefits they can bring.  

I see two major and positive trends in new technologies; large technology vendors entering the healthcare sector and startups and small vendors providing innovative capabilities.   

Major cloud providers and technology companies such as Apple, Amazon, Google, SalesForce, ServiceNow, etc. — are increasingly entering healthcare. They will provide more of the fundamental platforms and frameworks that health systems will build upon. 

Many startups and smaller vendors currently providing innovative capabilities will continue to do so but in a more integrated and broad scope. There has been a tremendous amount of innovation, but it’s very use-case-specific rather than holistic solutions, even when factoring in modest EMR-integration.  

We must be realistic that technology will not solve all our problems and unfortunately, will likely create new ones. For example: 

• Overwhelming the customer with confusing and overly complicated digital experiences reflecting convoluted internal processes. But the solution is known: relentless attention to detail, integration at the service layer, not the presentation layer (that would lead to a patchwork of styles), good architecture, and rigorous technical implementation. 
  
  
• Algorithms can be incorrectly selected, and models may be biased in various ways, which can lead to very damaging outcomes.   

Healthcare is facing a once-in-a-generation transformation opportunity to make care more consumer-focused. It is incumbent upon us, the healthcare leaders, to seize the moment for that change and create a robust strategy to implement the right technology in the right dimensions. If done right, we can minimize negative impacts to our patients, consumers, and staff, while offering higher quality care at a significantly reduced cost. 

The post Positioning Healthcare Providers for the Digital Age appeared first on HealthTech Magazines.

Geisinger is an integrated health care delivery system that is comprised of approximately 30,000 employees, including nearly 1,600 employed physicians, 13 hospital campuses, two research centers, an innovation institute, a medical school and a health plan with more than 600,000 members.

Geisinger is recognized as one of the nation’s most innovative health services organizations. In 2018, to continue Geisinger’s third generation of innovation, the Steele Institute for Health Innovation was created. The Institute is named after Dr. Glenn Steele, former Geisinger CEO and a pioneer of innovation.

The Steele Institute for Health Innovation forges a new generation of transformative, scalable, measurable, and sustainable solutions that improve health, care delivery, patient experience and lower cost. The Steele Institute is grounded in a belief that innovation is defined as a “fundamentally different approach to solving a problem that has quantifiable outcomes”.

The work of the Institute is organized by foundational pillars concentrated on three areas: health, care delivery, and payment transformation. We’re developing new care delivery and payment models. We are also working to develop innovative approaches to address social determinants of health that improve the health of individuals and communities.

The Steele Institute’s partner support services function as a range of teams and divisions that are aligned to access, utilize and maximize Geisinger’s assets.

The Data Enterprise division maintains Geisinger’s data warehouse, which houses data from the clinical system, the health plan and one of the world’s largest genomic biobanks. They provide routine and on-demand analytic reports to support health system and health plan operations and clinical care delivery.

The expertise of our cross-functional Product Innovation spans the areas of advanced and predictive analytics, informatics, software development, experience strategy, product design and product management. They have helped to  design and deploy a range of products including the Anticipatory Management Program (AMP) and EMS Go, an app designed and developed with individuals from EMS, Geisinger Trauma Services and Life Flight®, to help emergency providers route patients to the appropriate treatment centers.

The Artificial Intelligence and Machine Learning Lab focus on the development of technology and tools to identify populations at highest risk or those who would benefit most from specific intervention. The Steele Institute has an internal team working on developing our own artificial intelligence as well as partnering with external leaders in this space.  Over the next few years, the team will partner to build and deploy new algorithms to detect flu, pre-type 2 diabetes, and chronic disease management.

The Intelligent Automation team focuses on automating repetitive business processes that do not require higher-order human intelligence or intervention with the goal of reducing operating cost and increasing job satisfaction.

Human emotions and behavior are what our Applied Behavioral Insight team does best. They leverage theories in behavioral economics and cognitive psychology to help integrate “nudges” into workflows and processes to passively encourage better decision making.

The Steele Institute for Health Innovation is looking forward to guiding innovation efforts to improve health, health care delivery, and lower cost.

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