Electronic Health Reporter

By Anita Fernandes.

Data analytics is the next step in the evolution of healthcare as it uses data-driven findings to predict and address health issues. Healthcare data analytics can also help to keep track of inventory and access methods and treatments faster than conventional systems. Data analytics is often paired with health information exchanges (HIEs) to provide safer and more personalized care based on patients’ medical history, chronic conditions and medications. Healthcare data analytics software extracts, translates and synthesizes vast amounts of data to reduce costs, involve patients more in their own health and wellness and improve patient outcomes.

Opportunities and practical applications of data analytics in healthcare

Data analytics in healthcare relies on big data (vast quantities of digitized information) that gets consolidated and analyzed. The application of data analytics in healthcare has life-saving outcomes as it uses data of a subset or a particular individual to prevent potential epidemics, cure diseases and cut down on healthcare costs. Here are a few of the opportunities and practical applications of data analytics in healthcare.

1. Predictive analytics for personalized treatments

Predictive data analytics is the process of using historical data in order to make predictions that are personalized to each individual. Typically, analytics tools use information from millions of patients to help doctors make data-driven decisions and improve the delivery of care. Predictive data analytics helps to identify individuals who are at an elevated risk of developing chronic conditions based on lab tests, biometric data and patient-generated health data. Physicians can provide insight on lifestyle changes, wellness activities and enhanced services that can help patients avoid long-term health problems. This is particularly useful for patients with complex medical histories and suffering from multiple conditions,

2. Data analytics to advance telemedicine

Data analytics and telemedicine go hand in hand as it helps to empower physicians and patients and offers opportunities for remote patient monitoring and remote clinical services. Smart devices are the future of telehealth monitoring as they monitor a patient’s vitals in real-time and communicate with other devices and cloud health information systems based on data analytics to alert physicians about potential problems and provide analysis on possible interventions. Data analytics in telemedicine can help to predict acute medical events – this doctors to alter medication dosages to avert negative outcomes and prevent deterioration of patients’ conditions. Telemedicine also cuts down on costs, reduces the need for hospital visits and allows patients to live healthier and more comfortable lives.

3. Data analytics for real-time alerting

Hospitals have started employing clinical decision support (CDS) software that analyzes medical data on the spot and provides health care experts with suggestions as they make prescriptive decisions. However, in cases where patients are unable to make frequent hospital visits, doctors recommend wearables that collect patients’ data and send it to the cloud continuously. This data is analyzed continuously so that the system can identify potential problems and send real-time alerts to physicians. Doctors can then contact patients immediately to administer medications to prevent problem escalation.

Challenges of data analytics in healthcare

Healthcare deals with sensitive information, requires accurate information and can have life-or-death consequences which creates a hesitancy about overhauling existing healthcare systems to include data analytics. Concerns about privacy and data security is another significant barrier as patients worry that their data could put them at a disadvantage with insurers. The healthcare industry has been understandably resistant to make information available and easily accessible despite strategies to protect patient confidentiality. Unfortunately, this poses a barrier in obtaining the high quality data that is essential for data analysis and predictions.

Hospitals and institutions that have entrenched health care delivery practices are less likely to support the full adoption of data analytics. For instance, while data analytics can greatly improve the clinical decision making process, the development of decision support tools needs to focus on how these decisions are made so that they fit into the existing decision making processes.

One of the biggest challenges in the application of healthcare data analytics is that the responsibility for managing patients is split between their insurer and various healthcare providers. Insurers have their own incentives which center on costs which means that they do not function as the ideal agent for their patients. Federal policy can help to encourage providers to develop data analytics capabilities by prioritizing payment reforms.

Despite the challenges, the scope and usage of healthcare data analytics has been increasing as more and more healthcare organizations and providers adopt these solutions. The increase in lifespan also poses significant challenges to existing treatment delivery methods and highlights the need for data analytics in healthcare. Discussions with patients regarding data sharing arrangements and misuse prevention will help to encourage such practices which can improve care for all. Health care data analytics provides significant benefits to patients as it helps to reduce treatment costs, avoid preventable diseases, support overall health and wellness and improve quality of life.