New Age Healthcare facilities strive to provide the best care for their patients. Data Mining is one such important tool that can enhance patient satisfaction significantly. Read on to understand how Data Mining helps in improving patient care.
Introduction
Data pervades every part of our lives in today’s digital environment. It’s so pervasive and accessible that the thought of needing to mine it sounds strange. Data mining in healthcare, on the other hand, is a very real and useful activity. It aids in cost optimization, improving patient outcomes, and preventing fraud. The term has a slight twist, but we’ll get to that in a moment.
What is Data Mining in Healthcare
The act of sifting through massive databases in search of patterns and important information is known as data mining. It incorporates a variety of statistical analysis approaches as well as machine learning techniques to transform vast volumes of data into relevant insights. As an example, the software can assist clinicians in determining the optimum treatment strategy by comparing the symptoms of several individuals being treated for the same ailment.
According to experts, the global market for data mining software is on the rise, rising from $519.3 million in 2017 to a projected $1 billion by 2023. It’s hardly unexpected that these technologies are quickly becoming vital in the healthcare industry, and we’re going to find out why.
Benefits of Data Mining in Improving Patient Care
Today, the healthcare business generates around 30% of all worldwide data, and by 2025, this will increase to 36%. The capacity to make sense of such segmented data may provide a significant strategic edge to any medical company. Here are some of the most significant benefits of a well-executed medical data mining strategy:
1. Improved Physician Decision Making:
Lab tests are used by health care practitioners to enhance patient care. Analysts are increasingly using prediction models and mining methods to assist clinicians in identifying patient concerns. Often, lab data do not give this level of understanding on their own.
For example, researchers reviewed urine sample patient data using mining techniques. They were able to determine life expectancy rates for these patients by employing data mining. This data analysis method allows healthcare practitioners to recognise when people are sicker than they look.
A doctor can employ data mining algorithm insights to make a better therapeutic judgement and prevent additional harm to the patient.
2. Accurate Diagnosis:
Data mining in healthcare enables clinicians to make more conclusive, evidence-based diagnoses in less time. While an expert doctor is still required to make the ultimate choice, AI-enabled software can evaluate huge amounts of data in a couple of seconds. Content such as X-ray or MRI images, as well as blood tests, may be promptly analysed and classified to aid in the early diagnosis of cancers and other problems. When treating complicated illnesses with confusing symptoms, the speed and precision of interpretation can make all the difference.
3. Data Mining Mitigates Potential Drug Interactions:
Mining technologies are used by healthcare organisations to assist doctors in deciding when to give medicine. In order for a patient to take some medications, he or she may need to discontinue another prescription owing to potentially lethal interactions. Analysts may utilise healthcare data to prevent these interactions from occurring.
Because some interactions are uncommon, not all physicians are aware of them. Before developing any ideas, scientists can use big data analytics to uncover these fewer prevalent relationships. While data mining aids in the understanding of cardiovascular drug interactions, it can also yield information on other drugs.
4. Determining Purchase Patterns:
Patients do not always take the medication that their doctor has prescribed for them correctly. Mining tools are used by analysts to investigate patient purchase trends at pharmacies. They do additional research to see whether there is a link between this activity and adherence to doctor directions.
One study team, for example, examined patient analysis data from a pharmacy. In a warehouse, they analysed healthcare data and health records to determine the types of people who do not follow protocols.
Poorer adherence was associated with individuals who paid more and purchased something other than a prescription at the time of pickup. They were unable to forecast purchase habits since the correlation was insufficient to develop association rules. The application data they gathered, on the other hand, assisted them in increasing treatments in patients with poor adherence rates.
5. Improves Patient Outcomes and Safety Precautions
The healthcare business is constantly looking for innovative methods to save costs and enhance performance. Many analysts employ data mining to do this. While there is no single correct technique to increase quality and reduce inefficiencies, patient safety is linked to performance.
Researchers have learned from various case studies that the duration of stay and number of treatments are related to patient safety. These findings can be used by healthcare practitioners to improve safety measures and reduce patient re-visits.
Healthcare practitioners, for example, can utilise electronic health records and data mining to determine if two or more unfavorable events happened at the same time. This can assist hospitals in eliminating or minimising future safety risks.
6. Data Mining Reduces Fraud:
Many healthcare providers utilise data mining and machine learning approaches to reduce health insurance fraud. Traditionally, the claims department analysed each document for fraud but did not have enough time to check for red flags.
Analysts revealed that service providers can use data mining technologies to identify specific papers that may be fraudulent. This saves time and avoids false medical claims, which cost the government millions of dollars each year.
7. Data Mining Matches The Patient To Specialist:
Patients with unique diseases may not always be able to locate the expert they require for treatment. This is a source of frustration for both healthcare providers and patients. According to recent research, health care practitioners can employ data mining techniques to increase a doctor’s ability to diagnose these patients.
It can also identify particular doctors who are more likely to diagnose a medical problem. This decreases the possibility of error, saves time, lowers expenses for both the provider and the patient, and optimises each client connection.
Final Words on Data Mining Improving Patient care
Today’s healthcare business faces various obstacles. Costs must be reduced, providers must be more transparent, and the user experience must be improved. As big data becomes more prevalent, suppliers must apply analytics to enhance customer data access. It is also vital to guarantee that patient data is secure in order to comply with legislation.
Failure to accommodate patients with a set of best practices leads to litigation, false claims, and the severing of patient-provider relationships. Improvements in data gathering and mining efforts, according to experts, have the potential to change the healthcare business.
At Oriental Solutions, we handle medical data by following HIPAA compliance. To know more about how Data Mining can improve Patient Care, talk to us.
