Data analytics is the process of analyzing raw information to further use it in enhancing the organization’s overall decision-making ability.
It is often used in a range of industries, especially the healthcare one, where it helps the facilities to develop practitioners, predicts any outbreaks in the illness, and detects anomalies in scans.
And, do you know according to some recent statistics, medical data grows faster than financial services, manufacturing, media, and entertainment in general? By 2025, the compound annual growth rate of medical data will reach 36%.
Big data analytics software is created to manage this enormous volume of health care information. And only it can “curb” it today. Also, learn about Latest Google Chrome Updates 2024 by reading this article.
There are four types of big data analytics today. Each of them answers a specific question.
1. Descriptive analytics answers “What happened?” It looks at historical and real-time statistics and identifies trends.
2. Diagnostic finds out “Why did this happen?” It compares trends and finds cause-and-effect relationships between them.
3. Predictive offers the most likely versions of “What might happen?” The outcome depends on what type of artificial intelligence (AI) analyzes the data. The first option is “traditional AI”. In this case, the AI offers predictions only based on the input that its developers trained it on. The second option is “generative AI”. It learns from one piece of information and generates completely different — new and often unpredictable.
4. Prescriptive determines “What needs to be done to make this happen / not happen?” It uses clinical data and determines specific actions needed to achieve the goal.
Below are ten top case studies that describe what data analytics systems are able to do for healthcare.
A group of scientists investigated methods by which diseases are diagnosed from images. Python programming language was used for most of the methods.
The study mentions Massachusetts General Hospital. Its specialists used deep learning AI methods to analyze MRI images of the brain. AI analyzes several images at once and can do it much more accurately than a person.
Dmitry Baraishuk, the CINO at Belitsoft (a company with 20 years of expertise in digital healthcare) explains that the Python development company creates models for image recognition. These models study medical images or videos in real-time and help to detect diseases.
The models train in two ways. The first way is training on available image databases. The second way is this: developers use the client organization’s database. They classify and segment images via convolutional neural networks.
No-shows are not only a financial loss for the health care system. If a person occupies a slot in a doctor’s schedule and doesn’t show up for their scheduled appointment, they deprive other patients of the opportunity to get the care.
The NHS in Essex (UK) recorded losses of £5.9 million per year due to missed GP appointments. In the focus period from September 2022 to October 2023, almost one thousand four hundred people didn’t show up for their GP appointments every working day.
In America, the problem of no-shows also “hits” healthcare revenues and prevents medical staff from working effectively. The Dr. Luis Calvo Mackenna Children’s Hospital in Chile recorded that 29 percent of underage patients didn’t see their pediatrician, although they had an appointment.
This is a very high no-show rate. The researchers offered the hospital machine learning technology and analytics capabilities. The algorithms checked the patients’ gender, age, health insurance data, area of residence, etc.
It turned out that residents of Santiago (the poorest of the eastern communes) most often miss a scheduled doctor’s appointment. The experiment lasted eight weeks. During this time, the researchers reduced no-shows by 10.3 percent.
UnityPoint Health hospital network set a goal to determine recuperation and discharge dates accurately. It’s vital for hospitals, as these dates are the provider’s reference for managing hospital resources. Previously, the provider determined discharge dates manually.
This process was long and accompanied by errors in forecasts. UnityPoint Health uses a system that automatically predicts when a person is ready to go home. The system was trained on historical data of past patients.
Analytics studies info from the patient’s EHR and makes an accurate forecast. The automated solution prevents nurses from wasting time on manual entry. It saves them five thousand hours of work annually.
The accuracy of forecasts has increased by forty percent. The system now determines the discharge date for x4 inpatients within twenty-four hours of their admission to the hospital.
The University of Kansas Health System spent a lot of money to audit the patient registration process. It was imperative to enable precise medical billing and coding procedures. Staff reviewed the recipient’s registration process manually.
The University of Kansas Health System automated registration audits via analytics software. The result is that registration is correct the first time. Patients have fewer billing issues. The facility gets paid faster.
The UnityPoint Health hospital chain in the US used software and integrated a special tool into its EHR system. For example, this tool shows the doctor the results of lab tests or how the patient’s well-being has changed. The doctor decides if the patient needs to renew the prescription or prescribe a new one.
The analytics system also warns the doctor if the individual is taking other medications and these drugs don’t interact well with the drug whose prescription needs to be renewed. The process requests in just one and a half minutes. Medical staff can spend more time caring for the patient.
The Foundation for Health Care Quality (FHCQ) in Seattle has created a comprehensive registry that collects as much vital info about births as possible. One hospital that works with FHCQ has added more than fifty-four thousand records to the registry. The hospital used the registry data and noticed that the success of identifying newborns at risk of hypoxic-ischemic encephalopathy (HIE) is affected by the methods of testing the cord blood.
The doctors at the hospital tested it differently. Their inconsistency in testing methods prevented them from identifying as many newborns with HIE as possible. The hospital used registry data and analytics and followed FHCQ’s recommendations on how to improve testing rates. As a result, cord blood testing became seventeen percent better than in similar hospitals.
New York University (NYU) Grossman School of Medicine organized a group of researchers. They created a large language model (LLM). It studies information that comes from electronic health records (EHR). This LLM calculates the probability that a person will need to be readmitted within thirty days after discharge. According to the study, the model was five percent more effective than existing models.
Corewell Health in Michigan prevented two hundred patients from being readmitted when analytics algorithms had checked its medical records. It managed to save five million dollars.
Since saving was mentioned, here’s an example of a much more epic way to save money. Healthcare insurance company Kaiser Permanente implemented a new system. The system encouraged the use of EHRs and data sharing across all medical companies. It has improved cardiovascular outcomes. The number of lab tests and office visits decreased, and the company was able to save about one billion dollars.
Accountable care organizations (ACOs) try to be productive and get rewarded rather than penalized. They implement robust technologies to reduce per member per month (PMPM) costs and provide quality care and good outcomes to their customers. PMPM is the amount of money a provider or insurer earns or spends on each patient monthly.
MemorialCare used the PMPM root-cause analytics accelerator to determine what factors influence PMPM costs and what could be done to improve health care. The algorithms divided the individuals into two groups. The first group were patients who had recently been diagnosed with a disease for the first time by a physician. The second group were the individuals whose disease had become chronic.
The analytics system found that the people in the first group were more likely to choose urgent care than primary care. Because of this, the burden on emergency departments was higher. The care management team began communicating weekly with patients in the first group. The goal was to explain that primary or specialist care was needed in their case and to help them get it.
Healthcare organizations spend 15.4 billion dollars a year on the Hospital Effectiveness Data and Information Set (HEDIS) reports. The state of Wisconsin selected a nonprofit organization WISHIN to be the state’s umbrella organization for medical facilities.
WISHIN wanted to reduce the cost, effort, and time its providers and member payers spend on HEDIS reports. It took on the verification of primary clinical information sources. So its treatment plans could submit the right reports to the National Committee for Quality Assurance (NCQA) the first time and receive better ratings.
WISHIN partnered with one of NCQA’s certified record-keeping partners. It uses analytics algorithms to verify WISHIN’s information sources (hospitals, providers, pharmacies, etc.). The system automatically collects and verifies the data. Medical facilities no longer have to do this manually (e.g., check charts) and can avoid potential errors.
Analytics helps to solve not only the narrow tasks of each department but also to increase the motivation of senior managers. WakeMed Health & Hospitals is representative of such global benefits.
Its top managers use data from the platform, jointly make strategic decisions, and set relevant goals. They also developed a system of incentives and rewards for healthcare providers who achieved their goals. One of the company’s achievements was a 17-million-dollar cost reduction.
Thanks for choosing to leave a comment. Please keep in mind that all comments are moderated according to our comment Policy.
