Healthcare organizations have always been under pressure to cut operational costs, improve coordination, and deliver better outcomes. During the COVID-19 pandemic, critical equipment suddenly became scarce, demand for patient care skyrocketed, and it was impossible for organizations to predict or prepare for emergencies. During this chaos, data analytics for healthcare emerged to enable organizations to make life-saving decisions. Post-lockdown, healthcare IT analytics can be extrapolated to leverage technology to improve patient care at optimum costs.
Resolve end users' technical issues quickly. Identify and remedy lags in the ticket resolution process such as manual approvals or frequent ticket transfers. Gather key insights into the strengths and weaknesses of your support staff, and upskill them quickly to tackle sudden surges in ticket volume.
Closely track the usage of cloud, in-house, and hybrid infrastructure, and ensure the availability of storage and operational memory to effectively host critical applications around the clock. Build personas for user segments based on busy and lean periods in network usage, and ensure 99.9% uptime of networks at all times.
Demonstrate the ROI of IT operations by comparing IT budgets spent against quality of services delivered, decrease in downtime, increase in service quality, decrease in MTTR, and increase in end-user satisfaction ratings. Leverage healthcare analytics to increase ROI against every dollar spent.
Monitor patterns in data access, sharing, and privileged account use, and get early warning signs of changes in patient record access patterns. Calculate the real-time risk scores of personnel's (doctors, medical and support staff) corporate and BYOD devices, and seal security gaps with effective patching. Ensure compliance with internal, local, and federal laws.
Leverage built-in predictive models to forecast equipment and personnel requirements based on patient inflow. Use this info to plan IT budget proposals for the future. Plan digitization and modernization strategies so your organization can smoothly transition from paper to digital modes of patient care.
Notice a spike in operational costs or a decline in user satisfaction rating? Quickly tag relevant members of your team and decide contextually, from within reports, on how to deliver better IT services in less time.
Artificial intelligence (AI) and machine learning for healthcare IT analytics has made it possible to analyze enormous volumes of raw data and turn it into actionable insights for both clinical and operational improvementswith minimal human effort. AI-driven insights can also provide contextual relevance to problems and suggest remedial measures.