Healthcare Analytics

Healthcare analytics can be used to evaluate massive data generated within the healthcare industry. Predictive analytics is a process that uses machine learning to analyze data and make predictions. By extracting useful information from established relationships among variables, predictive analytics withdraws this information from an existing data set and predicts outcomes and trends.

OmniMD is able to analyze current and historical data to understand problems and help providers identify risks to their patients and opportunities to improve treatment.

Benefits of Predictive Healthcare Analytics

The use of analytics in healthcare is gaining momentum as the industry shifts to a value-based delivery model. Hospitals and clinics are searching for the ability to identify patient activity, reduce cost, and increase the level of engagement of both the physician and the patient.

Cost savings is important to most organizations. Predictive health analytics plays a vital part in reducing the cost of care. Organizations that combine financial data with patient analytics can pinpoint trends and spot patterns, helping to identify unnecessary transactions that do not compromise care.

How can OmniMD’s predictive analytics improve healthcare outcomes and reduce transactions?

Enhance Patient Engagement

Proactive hospitals and outpatient clinics understand that patient-centered healthcare requires building profiles that incorporate a holistic view of each patient. Predictive analytics will interpret, synthesize and analyze holistic patient information, leading to actionable insights that help predict and manage patients’ needs.

As a result, healthcare organizations can connect and communicate with patients through the channels they most likely prefer, thus delivering healthcare experiences tailored to each individual’s expectations.

Improve Physician Performance

Encouraging patient engagement through predictive analytics provides physicians with the answers they are seeking for individual patients. Analytics helps physicians determine the optimal treatment needed for each of their patients, leading to improved diagnoses, better outcomes and an effective use of each physician’s time.

Manage Risk Using Wearables

The advent of personal wearable devices, smart watches and heart monitors that measure and transmit vital clinical data to care management systems enable care managers to monitor patients around the clock. OmniMD can integrate with these devices, giving healthcare providers an enhanced view of their patients' health outside of the office.

By analyzing the data collected and building predictive models, providers can identify patterns and trends for the purpose of predicting injury or symptom exacerbation. Hindsight leads to foresight and early intervention can save lives, avoid complications and prevent unplanned hospital admission.

For example, a nurse with access to a patient’s personalized care plans would continually monitor the patient’s health status for potentially serious trends. If a dangerous trend in the patient’s vitals is predicted, the nurse receives an alert and can promptly reach out to the patient or authorized care givers via phone call, video chat or secure text. This allows for nurse or caregiver intervention before the patient even realizes there’s a problem.

Reduce Readmission Rates

Predictive analytics can reduce hospital readmission rates, by enhancing the care and attention a patient receives during their visit, through post-discharge and home transition.

Many studies have shown a direct correlation between patient outcomes and the length of stay in the hospital. The longer a patient is in the hospital, the more risk one has of infection. It is best to keep patients admitted only as long as necessary. Predictive analytics provides physicians with the data to redirect and enhance the stay of the patient in order to shorten their length of stay, if appropriate.

By analyzing patient information, predictive analytics allows physicians to not only identify the likelihood of a patient’s readmission, but develop outreach geared toward prevention. For example, if patients were hospitalized with heart failure, the hospital would personalize the post-discharge communication designed to educate them on appropriate diet, activity, warning signs and the benefits of complying with their medication directives.

Reduce Triage Costs

In addition to preventing hospital readmissions, the use of predictive analytics reduces the cost of care by enhancing the triage system. Hospitals have basic triage systems in place to assess the time and sequence in which patients are to be seen. By adding predictive analytics, providers can manage staff and resources more effectively. A triage system that incorporates predictive analytics has the capability to anticipate complications in patient care, which can lead to better patient experiences.

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