Ai and machine learning are working wonders in the Healthcare industry. From enhancing the patient experience with personalized care and paperwork made easy to doctors able to quickly diagnose medical problems and hospitals reducing the number of missed charges.
Learn MoreDeliver better care with lower costs by using machine learning recommendations that help medical professionals make informed decisions.
Machine learning can improve medical and administrative outcomes and operations, identify facility and patient financial risks, and retain patients.
Create excellent patient experiences by accurately forecasting staffing needs to reduce patient wait times, prevent patient churn, and effectively manage risk.
Doctors can now treat patients with even more accuracy and cut down on unnecessary procedures with machine learning’s medical imaging capabilities. A few use case examples are: Distinguish COVID-19 from pneumonia, TB, and unspecified lung shading; Detect COVID-19 and other infections and deliver a confidence score; Quantify the severity of the infection if detected; Non-intrusive technology; Renewable resource unlike test kits at a low cost with high accuracy and results in under a minute.
Fraud management in the Healthcare industry is a serious endeavor that usually involves whole teams dedicated to preventing fraud. Machine learning can help increase fraud management efforts by identifying fraudulent payment activity. For example, auditors can process perhaps 10% of all invoices. Machine learning, however, can analyze 100% of invoices to spot inconsistencies in the data and those invoices are then marked as requiring review. The auditor can then review the suspicious invoices, increasing the chances of catching fraud and saving the facility money.
The Healthcare industry consistently leaks revenue through missed charges, claims denials, and more. ElectrifAi helps to future-proof the revenue cycle and realize the facility’s full revenue potential. Combating limitations of traditional revenue cycles with pre-built machine learning models helps track, analyze and generate insights into your missed charges. Maximize visibility, increase revenue, reduce risk, drive successful and trustworthy decisions, identify reimbursable expenses, and much more.
Patient experience is a crucial element for a healthcare facility to keep operations running smoothly by bringing in revenue. If patients seek care elsewhere, it can hurt the facility’s financial stability. Use machine learning to know what patients are likely to miss appointments and proactively reach out, those patients who are likely to have recurring visits and plan treatment accordingly, those patients likely to schedule a telemedicine visit, know which patients to reach out to who are high-risk, and much more. Optimizing procedures to handle these use cases can reduce patient churn rates and drive revenue through cross-sell/upsell opportunities.
Healthcare facilities have many supplier contracts and invoices to manage and doing so manually can be a tedious and expensive task. Optimize contracts and invoices with advanced technology to speed up processes and reduce errors. Save costs through supplier contract optimization with machine learning, such as finding duplicate entries for the same supplier to decrease difficulty comparing contract terms to invoices. Machine learning can also identify when contracts expire and ideal times to renegotiate with suppliers. Additionally, invoice errors can be caught and fixed before paying the supplier, reducing the chance of accidental overpayment.
Doctors can now treat patients with even more accuracy and cut down on unnecessary procedures with machine learning’s medical imaging capabilities. A few use case examples are: Distinguish COVID-19 from pneumonia, TB, and unspecified lung shading; Detect COVID-19 and other infections and deliver a confidence score; Quantify the severity of the infection if detected; Non-intrusive technology; Renewable resource unlike test kits at a low cost with high accuracy and results in under a minute.
Fraud management in the Healthcare industry is a serious endeavor that usually involves whole teams dedicated to preventing fraud. Machine learning can help increase fraud management efforts by identifying fraudulent payment activity. For example, auditors can process perhaps 10% of all invoices. Machine learning, however, can analyze 100% of invoices to spot inconsistencies in the data and those invoices are then marked as requiring review. The auditor can then review the suspicious invoices, increasing the chances of catching fraud and saving the facility money.
The Healthcare industry consistently leaks revenue through missed charges, claims denials, and more. ElectrifAi helps to future-proof the revenue cycle and realize the facility’s full revenue potential. Combating limitations of traditional revenue cycles with pre-built machine learning models helps track, analyze and generate insights into your missed charges. Maximize visibility, increase revenue, reduce risk, drive successful and trustworthy decisions, identify reimbursable expenses, and much more.
Patient experience is a crucial element for a healthcare facility to keep operations running smoothly by bringing in revenue. If patients seek care elsewhere, it can hurt the facility’s financial stability. Use machine learning to know what patients are likely to miss appointments and proactively reach out, those patients who are likely to have recurring visits and plan treatment accordingly, those patients likely to schedule a telemedicine visit, know which patients to reach out to who are high-risk, and much more. Optimizing procedures to handle these use cases can reduce patient churn rates and drive revenue through cross-sell/upsell opportunities.
Healthcare facilities have many supplier contracts and invoices to manage and doing so manually can be a tedious and expensive task. Optimize contracts and invoices with advanced technology to speed up processes and reduce errors. Save costs through supplier contract optimization with machine learning, such as finding duplicate entries for the same supplier to decrease difficulty comparing contract terms to invoices. Machine learning can also identify when contracts expire and ideal times to renegotiate with suppliers. Additionally, invoice errors can be caught and fixed before paying the supplier, reducing the chance of accidental overpayment.
