The healthcare data analytics market is experiencing rapid growth, with a projected value of $122.20 billion by 2030. This is because healthcare facilities are increasingly adopting big data analytic tools to enhance patient compliance, lower operational costs, ensure treatment consistency, and achieve various other objectives.
Data analytics is a scientific process of analyzing raw data sets to identify similar trends and draw specific conclusions and inferences, and make predictions about the information it presents. In the healthcare sector, data analytics is used for discovering better treatment opportunities, operational management, and patient outcomes, among others. This article will speak to some of the key data analytics tools used in the healthcare industry, as well as the advantages, efficiencies, and other benefits that they bring.
Key Data Analytics Tools in Healthcare Industry
Healthcare professionals have access to different data analytics tools to collect and interpret vast medical and patient information. The most commonly used tools are:
Electronic Health Records (EHR)
Electronic Health Records (EHR) store patient medical history and provide key administrative and clinical data, including radiology reports, lab reports, vital signs, and past medical history. It provides a broader view of a patient’s care to ensure physicians can properly diagnose and devise a treatment plan. The primary benefit of EHR is that it can be shared with different medical facilities, making it easier for clinics, labs, and pharmacies to take accurate measures regarding the patient’s care.
Electronic Medical Records (EMRs)
EMR, or Electronic Medical Records, serve as the digital counterpart of a clinician’s paper charts and records, encompassing the patient’s medical and treatment history. Unlike EHR, EMR specifically focuses on data related to the hospital and does not include detailed information about lab tests and past checkups conducted in other healthcare facilities. Moreover, it is not in a shareable format, so other medical facilities cannot access medical history to take proper steps to improve patient care.
Personal Health Records (PHRs)
PHR is an electronic application that allows the patient or their caregiver, like a home health aide, to update and maintain medical records and health-related information in the digital space. Medical staff can access this information to track health goals and correctly diagnose and treat the patient. The PHR app also helps patients stay informed about their vaccinations, medications, and medical appointments.
Importance of Data Analytics in the Healthcare Sector
Healthcare data analytics play an important role in offering in-depth insights crucial in enhancing the patient’s quality of life and reducing further risks of diseases, worsened health conditions, or complications. Besides these, they are also used for the following purposes:
Reduced Medication Errors
Accurate prescription and medicine distribution are critical to preventing medication errors that cause 7,000 to 9,000 patient deaths annually. Medication errors can occur due to poor writing, miswritten symbols, and even writing an inaccurate medicine prescription. As a result of these errors, hospitals and physicians can face several legal consequences.
Data analytics can reduce this issue by helping assess patients’ records and matching them with prescribed medication to flag inaccurate prescriptions instantly. Healthcare professionals can also leverage this tool to maintain comprehensive medication records and utilize medical data sets, reducing errors and improving patient safety.
Accurate Predictions in Treatment Progress
Accurate prediction of patient treatment progress is essential to prevent medical errors and reduce costs. Healthcare facilities can utilize predictive analytics on EHR and bedside computer data to forecast health risks, chronic illness onset, readmissions, and surgical mortality risks.
Predictive analysis can also pinpoint patterns to suggest intervention programs. As a result, doctors can recommend preventive programs to eliminate the risks that can facilitate the onset of a disease or health condition. It can even predict if the patient is at high risk of readmission after discharge, facilitating better discharge decisions and reducing patient repeat stays.
Reduced Risks of HAIs
1 in 31 hospitalized patients contract Healthcare-Associated Infections (HAIs), increasing patient care costs. HAIs are infections that patients can contract from a medical facility while receiving healthcare for a separate condition.
Hospitals can use data analytics and geolocation to map HAI spread in different hospital areas and take proactive measures to prevent outbreaks. An example of this is the case of Augusta Health in Virginia, which combined EHR data with geospatial mapping to visually explore the spread of multi-drug resistant organisms (MDROs) among patients and hospital floors. Using this information, they devised a straight pathway between the ICU and the discharge unit to prevent the onset of MDROs.
Elimination of Unnecessary Lab Testing
Excessive and unnecessary laboratory testing is a pervasive problem in healthcare, with a significant number of doctors ordering repeated or redundant tests. Surveys reveal that many physicians admit to initiating tests despite believing their medical relevance is questionable. As a result of this unwarranted lab testing, patients face various adverse effects, including psychological distress, physical harm, and financial strain. Additionally, certain radiological tests pose a heightened risk of radiation-induced skin injuries and even the development of cancer.
Data analytics systems can prevent unrequired tests, save patients from discomfort, reduce treatment costs, and minimize hospital expenses. It can instantly offer data of similar results, so the physicians have immediate access to lab reports and don’t order irrelevant tests.
Timely Diseases Detection
Early disease detection increases the chances of treatment success while reducing patient care costs. With the help of a smart healthcare system and data analytics, healthcare professionals can identify subtle changes in the patient’s symptoms.
They can also use data analytics to conduct a risk assessment that helps timely identify potential chronic illnesses and diseases, such as cardiovascular diseases, diabetes, and more. As a result, the patients can get an accurate diagnosis and medical attention on time. Doctors can also use this information to guide patients on following a healthy lifestyle to prevent the onset of chronic illnesses.
Healthcare data analytics analyze raw patient data, groups it, and presents it to medical practitioners to help them make better patient care-related decisions. Healthcare professionals can use EHR and EMR tools to access all patients’ medical history and provide proper medical care, especially in accidents and emergencies.
Data analytics is also effective in reducing operational costs and preventing medical errors, such as prescribing the wrong medication and ordering unnecessary lab tests. The analytic tools are even reliable in predicting treatment progress, the spread of HAIs, and timely disease detection.