Data Science Health Applications
At the beginning of the 21st century, one of the biggest problems the healthcare industry faced was that of data handling. Some healthcare providers adopted the electronic health records system (EHR), but not everyone was on board.
These are systems that computerize and store medical records with the purpose of capturing, storing, transmitting, receiving, linking, retrieving, or manipulating data solely to provide health-related services to patients. The adaptation of EHR used to be debated upon but today, it is widely accepted and even sought after.
Data science has played a vital role in helping healthcare advance and shapes it for the future. Internet of things (internet of medical things, in this case) and connected medical equipment has not only changed how patient data is handled, but AI and ML integrations have also led to improved hospital workflow cost control and disease prevention — especially after COVID-19.
Here, we will discuss how data sciences have impacted healthcare by looking at some of these cases in detail.
Data Sciences For Precision Medicine
Data science implementations in healthcare are on the verge of transforming disease diagnoses and treatment. This is done via analysis of past-collected data and comparison to present patient data. This includes genetic data, disease contraction, vaccinations, what sort of healing treatments work, allergies, lifestyle, and potential outcomes and responses to a family of treatments.
This not only improves the level of healthcare provided but also decreases the chances of patients having to go through inadequate treatments.
A live example of how data science is helping doctors and physicians make sound healthcare decisions is Imec’s GAP (Genomics Application Platform).
This platform is focused on newborns, identifying any complex disorders that may arise in them at the time of birth. As soon as they’re born, procedures and treatments are started to tackle them and therefore improve the mortality rate.
Improvement in Healthcare Workflow and Performance
A major problem in healthcare facilities that patients aren’t usually aware of (but have to suffer because of) includes tensions between hospital staff. A major cause of these tensions is the lack of workflow orchestration and the overall dynamic (and unpredictable) conditions in hospitals and clinics.
Data sciences present a solution to these problems via cloud-based tools and applications, helping streamline processes considerably. A prime example of that is Aplacare’s AI platform.
The platform uses AI algorithms to create “smart health records” based on patient data and assigns relevant doctors to each patient. Its main focus is to ultimately enable Value-Based Care (VBC) by streamlining workflows and ensuring there is no tension between staff that reflects upon patients.
Healthcare-Associated Infection (HAI) Control
Healthcare-associated infection (HAI) is one of the most pressing issues faced by public health facilities worldwide — the US being no exception. If the name isn’t evident enough, HAI represents unrelated infections contracted by patients while under the treatment of another disease.
Over 100,000 patients contract HAI in the EU alone according to the European Centre for Disease Prevention and Control (CDC EU), and the figure is higher in the US (721,800 in 2011). While in most cases these infections get recognized and treated accordingly, these infections still are the direct cause of thousands of deaths each year.
This is even worse in developing countries where healthcare is mostly manual.
The World Health Organization has strict guidelines that hospitals should follow in order to minimize HAI, following these guidelines can prove to be difficult without a little help from technology. And that is where data sciences come in, offering invaluable solutions from time to time.
The main tools used for these solutions:
· Patient surveillance
· Real-time patient condition reporting, and
· Predictive models based on past and present patient data.
MONI is a prime example of an intelligent infection control tool currently in play.
It is linked to several documentation systems such as:
· The German NEO-KISS network (German National Center for Nosocomial Infections)
· The international Vermont-Oxford Network, the ECDC-affiliated Austrian Surveillance Network (ANISS Surveillance)
· The German counterpart KISS (German National Center for Nosocomial Infections),
· The Austrian branch (AUQIP) of the United-States based International Quality Indicator Project (IQIP).
Having said that, healthcare institutions can also implement it privately into their own systems for the sake of privacy. The platform imports raw data and converts it into surveillance information, which is then compared to the current state of the patient. In case any red flags are raised, AI quickly identifies what sort of HAI (if any) it is detecting and recommends immediate tests.
The reports generated by such platforms have been estimated to save $25 to $32 billion for US medical facilities alone.
Remote Healthcare — Monitoring & Care
COVID-19 has pushed the healthcare frontier from the physical to the digital realm. Although the initiative is rather commendable, at the beginning of 2020 it did present several limitations, which meant that patients weren’t always satisfied with the care they got.
Data science has enabled doctors to do routine check-ups much more thoroughly, and the prime factor at play here is the integration of technology (especially wearable sensors), or in short; the internet of things. Efforts are being made to sync watches and tablets that include essential sensors to track how the patient is doing.
This helps doctors (and even AI itself in many cases) to determine whether the patient’s condition is manageable at home or whether it is time to visit a hospital.
If and when there are any drastic changes in a patient’s health, these sensors immediately notify healthcare professionals, letting them make effective and timely decisions. This is particularly helpful when monitoring heart conditions, post-surgery patients, diabetic patients, or other high-risk patients.
The Future of Data Science Healthcare Applications
When it comes to data sciences and its application in healthcare, the industry is learning to rely heavily on data analytics and AI to help usher in a new era of improvements in healthcare services. Many facilities across the US and EU are implementing more and more cutting-edge technologies into the mix.
And as they continue to make further technological strides, the cost of this technology continues to decrease, thus making it more and more accessible.
