Why it’s time to prioritize a futuristic, patient-centered Healthcare ecosystem

As a healthcare organization, patients always need to be your top priority, so you find ways to use technology to avoid unpleasant things like patient leakages, and no-shows and improve patient retention to the best of your ability.

Patient experience, I’d say is the key to success going forward and all tech enterprises and ISVs seem to be facing that way.

Having seen people frequent hospitals during the pandemic, I thought penning a blog about how doing more with technology for your patients can not only help you drive patient retention, but also improve outcomes and take you closer to providing value-based care.

This blog talks about three technologies that you need to invest in to be on the right track to elevate patient experiences.

The healthcare ecosystem is a massive network meandering data sometimes peacefully and sometimes chaotically between hospitals (clinical data), payers (claims data), and patients (patient-generated health data). There is a need for the right kind of healthcare service to help this exchange happen amicably and create a next-level patient experience. The diagram I have chalked below lists the data that comes out of each of these systems and what you should be thinking about when you want to tackle each monster.

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Sources of healthcare data and areas of focus

Fig: Sources of healthcare data and areas of focus

Technology 1: Big Data Analytics for smarter decision making

Every industry has its own Achilles heel, and for the healthcare industry, I think it is the abundant but siloed data from various sources like clinical data from EHRs, labs and medical imaging, claim data from payer systems, and patient data from wearable devices, OEM IIoT devices. The biggest challenges here would be, one the volume as data is generated every millisecond and two, the structural variability thanks to disparate sources. These challenges are blockers in achieving seamless interoperability across the digital universe and offer to do their bit in obstructing care delivery.

With the help of appropriate software tools, big data comprehensively addresses these challenges by normalizing, storing, and interpreting data to derive actionable insights. This helps healthcare care providers and administrators achieve value-based care initiatives.

Population health management relies heavily on all kinds of data – data which shows the need of care required by the population, data that gives a glimpse of the extent to which it is available, and the data exploring the delivery modes where the right kind of care can reach the right kind of people.

This very need to analyze data coupled with government-led-value-based care initiatives can help in limiting rising healthcare costs. As a healthcare organization, it is pertinent to have a complete view of a patient’s historical information specifically as also population healthcare data in general.

For a healthcare provider clinical data can help deliver patient-centric care. Health plans can give deeper insights about claim and enrollment data where you can gain some perspective on costs, care patterns on a community level, etc.

A common data platform paves the path for possible healthcare innovations like evidence-based patient engagement and best practice care delivery approach, workflow optimization, proactively engage with risk-based population groups, and avoiding future complexities.

I don’t need to provide proof, since it will just seem ironical, but an evidence-based approach can turn alarmingly accurate by leveraging big data analytics and ultimately deliver personalized care. Backed by precision, we will then be able to do more with data for chronic illnesses like cancer, diabetes, and cardiovascular disease.

Technology 2: Machine Learning for better predictions

My well-thought-out diagram above already shows that healthcare data is supremely hybrid. Machine Learning algorithms run over this hybrid healthcare data and help predict ‘no-shows’ at the physician’s office. This means healthcare organizations can proactively manage these patients, reduce gaps in care, and mobilize healthcare resources for targeted delivery thereby optimizing operational efficiency.

Machine learning is going to also help you analyze the expected arrival time of patients setting foot in your ER. That is where those old and dusty hospital patient admissions data will make you feel like you struck gold. Running machine learning algorithms using that data can simply show day/hour level prediction of patients that arrive in the hospital for admission/ER visits using time series analysis techniques. This equips the hospital to manage infrastructure readiness to provide better care as well as promptly manage forthcoming influx of hospital admissions, especially in COVID-19-like pandemics situations.

The intelligence hatched out from deeper trends from this kind of data helps healthcare providers spend more productive time with patients and optimize personalized care.

Technology 3: Artificial Intelligence for efficient assessments

So, Machine Learning (ML) and Artificial Intelligence(AI) sometimes overlap like you can use radiology AI solutions to predict respiratory and neurology illness with higher accuracy as well as pathology AI to detect pre-cancerous stages which are currently beyond the human interpretation.

Where AI is most handy for healthcare providers is in Revenue Cycle Management (RCM).  Revenue cycle management is a pivotal step in the healthcare value chain whether we think payer or provider. It is highly transactional in volume and operationally challenging due to multiple health plan rules.

A patient interacts at multiple touch points in the healthcare system right from appointment scheduling to discharge. The data created along these touch points is crucial for a healthcare provider to create and submit claims as well as healthcare payers to validate and finalize reimbursements. Artificial intelligence solutions are competent in transforming this multiphase RCM process in a systematic manner operated by computer agents and healthcare parties can efficiently manage the task force to a more constructive care delivery approach.

Along with understanding, learning, and performing routine process like estimating out-of-pocket estimation, claim coding and several others; you can leverage AI  to detect fraud in claims and reduce improper claim payments through machine learning techniques.

In addition, healthcare payers can improvise their payment processes through data-driven anomaly detection techniques to map hidden behaviors and trends across all business lines.

With enhanced accuracy, both AI and ML will provide insights on some significant KPIs like charge lags, clean claim settlement ratio, pareto analysis by payer, physician/healthcare facility revenue estimation and projections.

In recent years, Generative AI has emerged as a game-changer across various sectors, and healthcare is no exception. With its ability to analyze vast datasets, create realistic simulations, and even generate new medications, Generative AI is paving the way for innovative solutions that enhance patient care, streamline operations, and accelerate research. Now, we will explore some compelling use cases of Generative AI in healthcare that are transforming how we approach medical challenges.

Use Cases of Generative AI in Healthcare

Fig: Use Cases of Generative AI in Healthcare

1. Drug Discovery and Development

One of the most promising applications of Generative AI is in drug discovery. Traditional drug development tends to be a time-consuming and expensive process. It can take over 10 years to take a brand-new medication to market. Generative AI can drastically reduce this timeline by predicting molecular structures and simulating how they interact with biological targets.

For instance, companies use AI to generate novel compounds that can be tested for efficacy against specific diseases, dramatically speeding up the initial phases of drug discovery.

2. Personalized Medicine

Generative AI can help create customized treatment plans by analyzing patient data, bringing in genetic data, medical history, as well as lifestyle choices. By generating predictive models, healthcare providers can anticipate how individual patients will respond to specific treatments. This leads to more effective and tailored healthcare solutions.

3. Medical Imaging and Diagnostics

Generative AI has shown great promise in taking medical imaging and diagnostics to the next level. It has done this by enhancing the accuracy and pace of image analysis. For instance, AI algorithms can generate synthetic medical images that simulate various conditions, allowing radiologists to train on a wider array of cases without the need for extensive real-world datasets. This capability not only aids in refining diagnostic tools but also helps in detecting anomalies that may be overlooked by the human eye. Moreover, by synthesizing high-quality images from lower-quality scans, Generative AI can enhance the clarity of medical images, thereby facilitating better diagnoses and ultimately improving patient outcomes.

To conclude, gathering data about each patient, applying all possible sets of analytical techniques to the data, and managing care is the first step to ensuring a patient-centric healthcare ecosystem. The last is using all the technologies I mentioned above to get your business and data into one single system so all the analytics you derive from your first step can be put to best use and you transform the life of one patient at a time.

Note: Healthcare integration capabilities such as integration of various systems using EDI, HL7, and FHIR can save a whole lot of time. These systems are also playing a pivotal role in transforming healthcare.

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