How to Improve Healthcare with Data Analytics

Healthcare costs are rising at an enormous rate.There are several ways to balance quality care with reducing the cost of delivery by using big data analytics to solve the demanding challenges of healthcare organizations.


With the growing acceptance of data analytics, providers, healthcare plans and liable care organizations are quickly moving toward an integrated and value-based care delivery system. We can see a general shift toward preventive care, together with a growing demand for accountable and agreed care.


Healthcare is a complex and constantly changing industry involving research and practice, experiments and established procedures, professionals, institutions, and providers of all sorts, and most important of all, the people they serve — both patients and doctors.  Over the last few years healthcare has developed through quick digitization, transforming much of the paperwork into electronic records and introducing computers to many aspects of physicians’ routines.


Digitization and smart analysis of data helps build a community of healthcare providers, however, embedding the innovation that leads to better healthcare, could also prevent cooperation and knowledge sharing. It may also lead to the rapid development of technology-based solutions without a careful analysis of the actual benefits and potential risks.


  • Personalized care. Patients often suffer when they have to change doctors and get in the same set of tests, questions, and procedures. This type of care wastes time, money and does not bring any visible improvement to patient’s condition. Every new patient brings gigabytes of data that include his genomic, proteomic and metabolic information. By analyzing large, complex and varied data that contain not just the patients' characteristics but also the results and the costs of treatment, hospitals can identify the most efficient treatment methods. This will decrease readmission rates and lower hospital expenses.

  • Preventive care over reactive care. Reactive healthcare cost much more than preventive care and trends show that people go to doctors when there is a problem. Using clinical data analytics, preventive care can be greatly improved. With preventive care, hospitals can reduce the number of patients who need costly emergency room visits and reduce their treatment costs. Predictive modeling, a part of clinical data analytics, is used to determine an individual’s health risks. Through these indexes, analytics can direct caregivers to provide advanced care that can help treat the problem before it acutely affects the patient's health.

  • Data based treatment. By using electronic health records to share patient information with doctors, hospitals are now more equipped to make better decisions about the care of patients. Through relevant information about patients and their medical histories, healthcare establishments can reduce post-op risk factors, such as surgical infections, poor physical conditions and allergies to medications. Such problems have created an unexpected financial load for hospitals and doctors through uncovered costs, and have prevented patients from enjoying quick recoveries. With clinical data analytics, such instances can be minimized to increase patient satisfaction.


Archer Software provides depth expertise in system analysis, planning, and prototyping management solutions for healthcare organizations. We can help your company by creating a central dashboard based on your unique metrics, or reformatting patient data like treatment details, appointment history, medications, specialists’ comments, and lab results like CBC, HCT, Hg and others according to HL7 and CCD standards.


You can learn more about our healthcare-related projects here.