Rising healthcare promises have been tied to cloud technology in the most recent tech-talks of the town. While the majority of care providers are not holding their breath due to previous disappointments, we wanted to translate the often vague statements made into discrete simplified processes for healthcare.
Healthcare is riding a wave of digital transformation that has brought about revolutionary processes of data management and care delivery. Moving from paper-based records to a digital format, the first wave took us from disconnected facility-based care to integrated smart care with increased coordination and population health activity.
The second wave enabled better patient experience with omnichannel communications and interoperable data sharing applications. Empowering patients and clinicians with analytics, the recent wave has health organizations leveraging real-time data-driven solutions, artificial intelligence, and cloud services to align with the culture of preventive and wellness-centric care.
The cloud will be central to future digital transformations in healthcare. What is uncertain for many is what specific, new cloud services will be developed and why are healthcare organizations now – and foreseeable future continuing – to opt for cloud-based technologies.
Why are health organizations leveraging the cloud?
We have been in the process of transitioning from fee-for-service to value-based care over the past decade. The industry is further planning to move from disease-based episodic care to preventive care in future years. To achieve that goal, several additional factors need to progress.
The healthcare system of the future will be more consumer-centric and value-driven. It will use real-time data to generate actionable insights, and data technology will play a crucial role. Cloud technology promises to improve performance enhancement and healthcare data analytics overall.
Health systems have a need for increased data capacity, and the cloud promises almost unlimited data storage, easy accessibility, and enhanced cybersecurity. As health organizations are expanding into a variety of digitized services such as virtual care, wearable devices, telemedicine, and smart AI assistance, the data per patient expands.
The cloud is a single point of access to patient information, to multiple doctors and medical services at the same time, that boosts not only real-time coordination but also ensures data security for hospitals and patients.
Gartner, in a recent healthcare cloud services report, highlighted how provider leadership has moved from skepticism to acceptance of the cloud as a service delivery model. In what ways is the cloud benefiting the healthcare industry?
After the enactment of the Affordable Care Act, insurers had to cut down on the “cherry-picking” of members and not provide insurance to just low-risk individuals. To some extent, the scope for earning high-profit margins had decreased for health insurance companies. This rule created an imperative for them to look for ways to curb expenses in other ways.
As a result, influential insurers came up with an innovative idea to merge with their contemporaries. Mergers and acquisitions reduce the competition and empower payers to negotiate better with the providers.
However, a lot of Medicare Advantage (MA) markets are served by just one or a small number of insurers and the competition is already bleak. If the few existing insurers also lobby to negotiate contracts, the providers wouldn’t stand a chance to get a decent deal.
US healthcare dynamics are already far from ideal with costs soaring high and quality parameters below most developing nations. The lopsided power play between providers and payers can exacerbate the existing healthcare problems.
To prevent this, the government is making it a point to put brakes on major insurance mergers and acquisitions. At the beginning of 2017 U.S. District John D. Bates ruled against Aetna’s acquisition of Humana. Along with that, the Anthem–Cigna merger was also stopped from going through. It seems like it’s time for payers to think beyond this strategy of creating an oligopoly. It also means that insurers have to compete with each other instead of relying on their collective clout.
Gaining an edge over competitors with improved star ratings
Now that there is no way for insurers to earn strong profits other than by capturing increased market share, they need to look for ways to increase the number of enrollments. For MA plans, their best shot to grow their member enrollments is by achieving credible star ratings. Medicare’s Star Rating system was developed to provide Medicare beneficiaries some concrete insights about a plan’s performance. Every year, CMS evaluates the performance of each MA Plan on quality and cost measures and rates them on a scale of one to five stars. The more stars a plan gets, the more appealing it appears to the beneficiaries, which leads to increased enrollments.
Every health plan aims to achieve maximum operational and cost efficiency and tries to create lucrative offerings for the members. However, unless they are able to do it better than other health plans, their efforts will not bear fruit. The first step to improving their Star ratings is to assess the performance measures of other health plans.
Evaluating the performance of MA plans over the last few years
In 2011, only 24 percent out of all MA Plans got 4+ Star Ratings. By 2018, this figure grew almost by a whopping 50 percent. In a bid to perform better, the health plans brought remarkable changes in their performance. As a result, member enrollments also increased by 17 percent in this tenure.
Since the inception of the Medicare Advantage, some measures were modified. However, there were 22 measures that remained consistent between 2009 and 2018. On calculating the average Star ratings of each measure, it was revealed that the average Star ratings had improved by 0.56. The average outcome measures had improved by 0.45 stars, the process measures by 0.49 stars, the patient experience measures by 0.55 Stars and the access measures by 1.12 stars.
Overall, most of the measures showed some improvement. Since 73 percent of the health plans have ratings above 4 Stars, it can be established anything below this can gravely impact the number of member enrollments. On top of that, to match the average performance scores of other health plans, health plans have to earn an average of at least 3.5 Stars.
Creating strategies to perform better than other health plans
An overarching picture of how all the MA plans in the country are performing can be helpful in revealing the measures you need to work on. However, understanding the area-specific operational nitty-gritty is important to find out what steps you need to take to improve the performance of your health plan.
Comparing your plan with top-performing health plans in your area and diving deeper into their measures can unveil what they are doing to perform better than others. This information can be instrumental in devising winning strategies to score star ratings that are better or at the very least at par with other high-performance plans.
The provider community strives day and night to improve patient outcomes and contribute to the dream of value-based healthcare. However, the complexity of chronic diseases renders strategies ineffective and prevents them from reducing available utilization. In the US, chronic diseases account for 75 percent of all healthcare spending, to the tune of $3.5 trillion. In fact, every 6 out of 10 US adults is living with a chronic condition.
And, the costs are going to inflate in the future as well
By 2030, there will be more than 77 million+ people above 65 that necessitates Medicare coverage, which also calls for better chronic care management measures. If high-risk populations are identified now, the US healthcare can be better prepared to meet care expectations in the future and contain the costs for good. That said, a myopic approach to chronic care isn’t going to cut it. Let’s take a look at the loopholes in current chronic care management programs.
Pitfalls in Chronic Care Management
Fixation on short term goals
Effective chronic care management requires providers to focus on long-term well-being and stabilization needs of patients. However, the Affordable Care Act incentivizes providers for a reduction in 30-day re-admissions post-discharge. To witness a visible and landslide impact in chronic care management, providers must be looking for a mechanism that can track care management for high-risk patients beyond the 30-day readmission policy.
Less accommodation for comorbidities
Multiple chronic conditions have associated comorbidity that can increase the costs in the long run. Healthcare needs to inch to a robust system that takes into account the needs of comorbid patients. Mckinsey research suggests that 71% of patients with heart failure have hypertension, 37% have diabetes, and 53% have hyperlipidemia. These stats indicate that providers have the opportunity to go upstream and engage with these patients while they have a low-morbidity condition.
Inadequate risk stratification
Risk stratification is majorly centered on the needs of high-risk patients and often negates rising-risk patients. While preventive mechanisms for “high-risk” and “rising-risk” patients require a demarcation, specialty care and telehealth don’t promise a similar ROI for both patient pools. Aside from this, additional factors such as Social Determinants of Health are not an integral part of every risk stratification algorithm that results in skewed chronic care management plans.
Fragmented care delivery
A lack of coordination renders chronic care management ineffective and many a time, patients end up receiving clashing treatments that can lead to increased costs.
Primary care vs. specialty care
Primary care providers often face a hard time figuring out when a patient can be successfully managed in a primary care setting or qualifies to be under specialty care. Taking the right call between the two often becomes the reason for higher costs because of an increase in acute care utilization.
One of the many aspects that insurers focus on to create more value through their health plans is to improve communication with the members. In the era of growing digitization, most payers have started to offer online services. However, many beneficiaries still use traditional channels to interact with insurers.
Does it imply that members are averse to using digital channels for communication?
On the contrary, members are, in fact, more inclined to using digital channels than ever before. A survey revealed that 77 percent of consumers would like to pay their health insurance bills through an online portal. If members have the option to use digitized modes and they still continue to use the traditional modes, it clearly indicates there is a problem.
What prevents beneficiaries from using digital channels?
At this point in time, multinational giants such as Amazon and Google have made customers accustomed to unbeatable customized digital content. If members are still using old forms of communication, that is bad news for health plans. The probable reason behind this is unsynchronized information on offline and online channels.
Take an example of a member who has been communicating with their insurer through a call center and wants to shift to online communication. For that, they would have to share all their details on the new channel all over again, despite the fact that their information was already available to the insurer. This may lead to frustration because this interaction is neither convenient nor fast. As a result, they wouldn’t want to switch to a channel that makes the process more cumbersome than before.
The solution? Building omnichannel capabilities
For digital channels of communications to thrive and boost member experience, payers must work on developing omnichannel capabilities. Omnichannel communication can allow members to switch seamlessly between online and offline channels at their own convenience, without any additional steps. Even though most health plans offer digital communication, can only creating omnichannel communication maximize its value?
“Pop health is still a pretty manual process. Having a dedicated solution, let alone a dedicated analytics platform, to address pop health is not as widespread as one might think.” — Brendan FitzGerald, research director, HIMSS Analystics
When I first heard this line, a number of thoughts came rushing into my mind around the different population health management strategies deployed today. In my experience, I’ve noticed a lot of variance in these strategies, and somehow, all of them traced back to data integration.
Some regions focus on leveraging their existing EHRs solutions. Other areas attempt to find the best point solutions and try to integrate them together. Many other organizations are looking for partners to help build and deploy more targeted solutions. Ultimately, these organizations are trying to find the right solution to achieve sustainability in these changing times.
Healthcare data: The problem of plenty and inefficient solutions
One problem that I usually see is that there has been a lot of talk around providing a holistic solution — and the industry isn’t even close. Healthcare organizations have already drained millions of dollars in the hopes of improving outcomes through new technologies, and I think there is a dire need for a change in what we promise to deliver. What organizations need now are infallible strategies that focus on achieving a better outcome.
It is never about just integrating the healthcare data!
There is a buzz in healthcare around aggregating data. However, they are far from making sense of this data.
The question which we should be asking right now is how we can help save money and continue to deliver better care. The easiest way to analyze the progress of organizations is by examining the returns on investment in terms of outcomes and revenue. And this return is only possible if organizations are successful in activating this data to ensure that every member is utilizing it to their fullest potential.
Unless healthcare members have a holistic pool of information regarding every activity in their healthcare network, they cannot ensure that they remain at the top of every process.
Taking long leaps to establish transparency in healthcare
A few months back, a tweet from the CMS Administrator, Seema Verma, took everyone by surprise, and the concept of siloed healthcare took a significant hit. Value-based care is the future, and #WheresThePrice laid the foundation for transparency in terms of cost, expenditure, quality, and data.
It is time we took this concept of transparency to a broader level, moving beyond merely the pricing to ensure the transparency of healthcare data. After all, only the right access to the correct data can result in the right outcomes.
What is that one factor that separates one patient from another? Can one identify why two patients with the same illness but from different regions respond differently to a particular treatment? Do we need to cater to the needs of patients even after they leave the clinic?
These questions have always intrigued not only the physicians but every member who is involved in the care journey— care teams, communities, social workers, even patients themselves. And the answer lies in just one fact— even if these two patients appeared similar on paper, their lifestyles are very likely to differ: socioeconomic status, gender, race, ethnicity, family structure, and education.
All of this comes down to just one term: Social Determinants of Health.
This is one of the prime problems that has kept healthcare organizations in a situation of dilemma.
We are way past the statement that SDoH is just another hype
Have you ever tried to score a home run with one hand tied behind your back? This situation is similar to the condition of healthcare organizations in the value-based ecosystem. They are trying to get 100% of the task of healing a patient done with just 50% of the insights.
Social determinants matter because they can affect the health of the population residing in a particular region, for better or for worse. We have countless studies that show the importance of social determinants, yet we are not able to properly address them because we are not able to answer these questions:
How do we address the challenges that we don’t even know exist?
Who is responsible for addressing these challenges?
Is there any ideal strategy to address SDoH?
No matter how famous they are in healthcare, working with SDoH requires a drilled-down approach and something that we have in abundance- healthcare data. This data can be leveraged, and with the use of predictive analytics, organizations can accurately measure the at-risk population and advance preventive care methods in the ecosystem.
The best way, I think, is to look at this picture with a magnifying glass. Traditionally, the endpoint is the state-level analysis of SDoH. However, it is not the end but the beginning of the study that should go to the zip code level.
Here are some of the most interesting stories of how the leaders in the field of addressing the Social Determinants of Health addressed the populations’ needs and did the undoable.
What was the Humana way to deal with the non-clinical factors?
Humana has the Bold Goal Initiative, which is a population health strategy that is aimed at improving the health of the communities and making them 20% healthier by the year 2020. Their Healthy Days surveillance process is a robust and scalable metric. Based on this, they found that food insecurity and loneliness were among the top contributors to the total unhealthy days among the population they serve.
With their holistic and comprehensive approach, they built an analytic intervention pipeline to address these issues. One instance is their intervention with Papa Inc., where they connected college kids to seniors who needed companionship. As a result, 94% of members stated that the Papa Program helped them to feel more socially connected.
Humana was able to reduce the number of unhealthy days from 2015 to 2018 by simply addressing the non-clinical aspects of care delivery for their population.
Performance of Humana’s seven original Bold Goal communities (2015-2018) – Humana Medicare Advantage members
How MercyOne PHSO took the understanding of non-clinical factors from the zip code level to an individual patient level?
MercyOne PHSO, one of the largest ACOs in the Midwest, wanted to know the factors affecting their patients. They took the simple concept of asking the right questions and leveraged it to understand their patients.
While their patients entered the hospital or examination room, they asked them to complete a survey consisting of questions that depict the factors that affect their patients’ health, such as:
In the last 12 months, were you worried that your food would run out before you got money to buy more?
What is your living conditions today?
Do you face any difficulty in reaching out to your doctor?
Imagine your favorite football team is in a real neck-to-neck with another team, and the game could tip in anyone’s favor. It is the last minute, and in an insane turn of events, the quarterback throws the ball in the air, hoping the player in the end zone could make a touchdown. Instead, the reckless throw results in confusion, the guy in the end zone gets tackled, and the game ends in disappointment.
Now, let’s step out of football and look at these statistics that show a little picture of referrals in healthcare:
Only about 50% of referrals result in a completed appointment
Less than 25% of referrals are completed as intended by the referring provider
In case one, the player didn’t score a touchdown, and in the second case, the patient didn’t end up with the right provider and the treatment. The reason being the process— a reckless throw and an inefficient referral procedure.
Most healthcare organizations lose about 30% to 60% of patients on account of inefficient referrals. Value-based care is expected to become the leading payment model by the year 2020, and healthcare organizations cannot afford losing more than half of their revenues due to reduced referral leakages.
How do you know that your referral management needs healing?
Imagine a situation where a patient, in his early 60s, suddenly suffers from severe abdominal pain. He goes to his doctor, and the doctor directs him to a specialist she knew out of her professional knowledge.
Now the situation can unfold in many ways, where the patient might end up getting treated or the exact opposite of it. In all the scenarios, the part where things might go wrong is the process of referring the patient. The problems that these stakeholders might face include:
The inability to identify in-network providers
Lack of proper patient information
Limited access to information flow among providers
Reliance on age-old techniques of fax-based referrals
… and many more.
Now the question is: ‘What is the solution?’
It all boils down to just one thing— having the right data. Imagine you visit your doctor. The moment you tell him your problem, he looks into his screen to look for the right specialist. In just one click, he gets all the correct specialists in a listicle format. And all he has to do for the rest of the story is just click on the ‘Refer’ button.
Seems undoable? Actually, all we need is a data-driven strategy.
Don’t just plan your data but also your approach
It is never about just knowing the patients but understanding them, their health, their socio-economic condition, and their care journeys. All of this is not possible if we do not have access to the right data. Whether it be a lab test or spiking blood pressure— nothing should be left undetected.
Easier it is for providers to understand, efficient will be the referral
You cannot expect the rest of the process to be perfect if the beginning is imperfect. If the provider is stuck finding the information, not only will this delay the referral but also increase the chances of errors. What they need is a single screen view of specialists in a list that includes every detail such as geography, specialist ranking, availability, and fees, among others.
Connecting communities and care teams to deliver the best care
It is crucial that care teams and communities remain aware of the events happening in the patients’ care journeys. They need a streamlined tracking of patient referrals at the clinical or patient level. It will reduce the turnaround time for escalations.
The patient lost in the process is the revenue lost
The right referral strategy includes two significant aspects:
Increasing the visibility into the process to the patient
Using advanced analytics tools to develop a lens into the referral process
What they need is a simple reminder that enlists all the details regarding the visit and gives timely updates to them regarding the specialist and the appointment date. Organizations can increase patients’ access to telehealth services by allowing plans to propose the use of telehealth services instead of promoting in-person visits.
The healthcare circles in the United States are reeled up by debates around the need for price transparency.
The federal agencies are coming up with regulations.
Healthcare associations are weighing in their concerns.
Physicians, patients, and economists – everyone is articulating the pros and cons in a rather plausible manner.
Wait. What has triggered this rush towards transparency?
To begin with, the healthcare costs across the country have gone from “extreme” to “unreal” in the last two to three decades. A regular MRI scan, for instance, costs twice as much as it does in Switzerland, another country where healthcare is considered “notably expensive.”
Worse still, one simply cannot tell how much money they might end up paying at a healthcare facility at any given point. A broken bone can take thousands of dollars to get fixed or at no cost at all – depending on a dozen factors that can vary drastically with each patient.
Frankly, there is no single moment that burst the bubble around the soaring healthcare costs. In many cases, what hurt patients more than the total cost of a procedure is the out-of-pocket expense that they are made to pay. The focus today has shifted to one fundamental question – how much money is justified for a given care procedure; and are we entitled to know it or not?
Cut to 2019, a movement to make care prices transparent is shaking the establishments across the US.
What is the government saying?
The government has taken the onus of ensuring transparency in healthcare prices. Last month, the White House issued an executive order aimed at making payers and providers publish the cost of each procedure available at their facility. The government believes that this step can get a long way in making patients take more informed decisions regarding their health and eliminate the opacity regarding the cost associated with such processes beforehand.
The intent here is to provide patients “access to useful price and quality information and the incentives to find low-cost, high-quality care,” something that can be a giant leap forward in the direction of enabling cost-effective care.
One of the greatest challenges in healthcare is keeping up with the changing landscape. Considering only since the beginning of 2019, the Centers for Medicare and Medicaid Services (CMS) and other federal agencies, such as the Office of National Coordinator of Health IT (ONC) and the Department of Health and Human Services (HHS), have introduced a number of rules as a measure of upholding their goal of empowering patients and enhancing healthcare efficiency. We’re at a very critical juncture in healthcare and from a regulatory perspective, there are a few key rules that merit a special focus which will have a great impact from both a clinical and financial standpoint.
The MyHealthEData Initiative in 2019
The MyHealthEData initiative, launched in March 2018, aims to “empower patients by ensuring that they control their healthcare data and can decide how their data is going to be used, all while keeping that information safe and secure.” Only a few days back, CMS upped the ante for better data access by expanding this initiative and announcing the pilot of “Data at the Point of Care.”
The Data at the Point of Care (DPC) pilot will be connecting providers with Blue Button data, where providers can access claims data to learn more about their patients and their previous diagnoses, procedures, and prescriptions. While providers had to comb through several hundred data sets previously, the DPC program would aim to make access to data easier and right within their workflows.
This announcement follows the relaunch of the Blue Button initiative, or Blue Button 2.0, that grants access to health data and enables patients to send that information using FHIR-based healthcare apps.
In a nutshell, these moves come as an overall push from CMS to promote better access to data and 100% healthcare interoperability. In addition to enabling data access, CMS has also been targeting information blocking, as reflected by 2019 MyHealthEData updates. With these measures, both patients and providers will have the required insights to make more informed healthcare decisions.
The Trusted Exchange Framework and Common Agreement
In April 2019, ONC published its second draft of the Trusted Exchange Framework and Common Agreement (TEFCA), focusing on three high-level goals:
Providing a single ‘on-ramp’ to nationwide connectivity
Enabling Electronic Health Information (EHI) to securely follow the patient wherever needed
Supporting nationwide scalability
TEFCA is basically a common set of principles which serve as “rules of the road” for nationwide electronic health information exchange across disparate health information networks (HINs). The framework, which was mandated by the 21st Century Cures Act, provides a set of policies and procedures along with technical standards required to enable healthcare data exchange among providers, state and regional HINs, and federal agencies.
By Abhinav Shashank, CEO and co-founder, Innovaccer.
The Johnsons were blessed with twins the day before; two healthy baby boys, haphazardly named Jill and John in the health records. Definitely, this marks the start of pediatric services in the family. Hospital records set for the twins hardly mark any difference, gender, weight, parents, address; all records read the same. The only visible difference is a skin allergy with the second baby.
Their names were changed to Jack and Ross in a
month, and records got multiplied by two. Vaccinations done within the first
month were registered in the records of Jill and John, while Jack and Daniel
got registered under fresh EHRs.
pediatric space ripe enough for Machine Learning?
How should the healthcare industry deal with
data redundancy or data hop, and maintain data integrity to ensure reliable
records? This is a real serious concern for pediatric organizations.
However, to our rescue is machine learning technology aiding the critical issue of record matching and streamlining medical procedures in child healthcare. ML has the potential to revolutionize the pediatric care ecosystem and assist the major challenges in healthcare operations of the young population.
With the global healthcare market estimated to reach a sweeping $11,908 billion by 2022 and fast-growing problems in the younger population, there is certainly a vast frame of exploration for pediatric focus and care delivery for the young. Being a continuously evolving age group with tailored and sensitive healthcare needs at different stages of growth, the pediatric population is most challenged when it comes to successful reforms and insights.
EHRs doing injustice to the future of healthcare?
Kids from their birthdate are expected to face the EHR duplicity that scatters their record and essential medical data. The key facts of a newborn like weight, height, allergies, among others, are stored in an EHR that is occasionally hopped a month later, with a permanent name signing in.
Once a new EHR is registered with the new name, all medical information of the previous few months gets disconnected. This has a challenging impact on the entire care protocol. The critical notch here is incoherent vaccination and immunization information of the growing baby. Not only does it lead to seemingly real care gaps, but also ripples out to erroneous procedures and increased health costs.
Machine Learning is transforming the way
services are delivered globally. Detecting the minutest of factors in an
outcome, and cascading the learning over huge data, it can provide us with
crucial considerations which are evidently present but still go unnoticed by
us. ML is helping to deliver accurate algorithms for all domains. Applying ML
to pediatric care is sure to transform the current scenario of care delivery
for the younger population.
are the major challenges pediatric organizations are facing?
We need strict adherence and care, not only to
ensure healthy children but also to ensure optimized care procedures for them
in the future. However, there are a lot of shortcomings in understanding and
implementation of the medical requirements of the population aged 0 to 18.
The major challenges in this regard are:
Most pediatric organizations today do not have precise and distinct health measures to evaluate the younger population. We need measures that can efficiently assess the patients on their growth-specific checkers, respectively.
Patient records at different stages are difficult to merge, with inadequate data-merging proficiency.
Data hop in EHRs during record matching or establishment. This is of critical concern for babies and toddlers who need consistent care episodes.
Lack of customized reach to parents for time-sensitive immunization and vaccinations. This leads to missed appointments, which leads to complications and increased costs over time.
Care plans including uncertainties to manage intelligent adherence. This will enable strong network functionality and improved care.
Flexible and optimized timeline for care delivery.
Currently, about 50 percent of children under five years of age attend out of home care. Throughout childhood, children receive care at daycares, check-ups at community places, have physician visits at different pediatric facilities, among others.
It becomes essential to compile entire patient data at a single place to avoid redundant and erroneous procedures. According to the American Health Information Management Association, an average hospital has about a 10 percent duplication rate of patient records. A study by Smart Card Alliance in 2014 projected that about 195,000 deaths occur yearly in the US because of medical error, with 58 percent of them being associated with “incorrect patient” errors.
Machine Learning truly have the answer?
An article in the AAP News and Journals Gateway mentions that only 71.6 percent of young children in the United States have completed their primary immunization series. Moreover, evidence suggests that 10 percent to 20 percent of young children receive more than one unnecessary and extra immunization. Evidently, scattered records lead to a lack of timely, accurate and complete immunization. This can have serious repercussions on the health and care protocol of the patient, in addition to increased medical costs.
Machine Learning can nourish the split needs
and resolve the errors of pediatric healthcare in different domains:
Automatic Triggering for Episodes and Immunization: ML algorithms can be developed to track and prompt parents for necessary episodes and immunization. This will ensure timely care episodes.
EMPI Matching: Enterprise Master Patient Index is a database of medical data across departments and healthcare organizations. Machines trained in pediatric EHRs can develop a robust algorithm to match patient records across hospitals and unify them.
Streamlining Vaccinations: ML algorithms can regularize time-sensitive vaccination arrays for different pediatric categories as decided by the World Health Organization.
Scanning Data Hops: ML algorithms can detect data gaps in procedures, and point out critical consequences enforcing timely merging of EHRs.
Predicting Episodes and Costs: ML algorithms trained with localized pediatric data can detect underlying factors for an episode and predict the average costs for unforeseen episodes.
The pediatric population is foundational to a
healthy nation and demands our attention to reform its split functionalities.
Machine Learning can bring about unimaginable amendments in our current
pediatric care management and delivery. Data, which is foundational to all
ventures in the healthcare industry, can be merged with ML to close all care
gaps and invest in a healthy tomorrow.