The Johnson’s 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.
Is the 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 $11,908.9 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.
How are 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.
What 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% 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% duplication rate of patient records. A study by Smart Card Alliance in 2014 projected that about 195,000 deaths occur yearly in the US due to medical error, with 58% of them being associated with “incorrect patient” errors.
Does Machine Learning truly have the answer?
An article in the AAP News and Journals Gateway mentions that only 71.6% of young children in the United States have completed their primary immunization series. Moreover, evidence suggests that 10% to 20% 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 road ahead
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.