Patients today expect consumer grade experience from their providers. They expect efficiency in any interaction with a hospital – all the way from registration to physician interactions and finally to billing. Patients are also very informed of medical conditions, treatment options and risks, and expect high value personalized care from physicians. [click_to_tweet tweet=”According to some estimates, 82% of patients would switch providers after a bad experience.” quote=”According to some estimates, 82% of patients would switch providers after a bad experience.”] Providers are looking at innovative ways to meet these expectations and keeping patients satisfied.
One of the challenges providers face in extracting insights is the fact that data is distributed in various silos – EHR systems, Salesforce Health Cloud, and external datasets like IMS Health. Some legacy EHR systems make integration very difficult. SpringML’s approach to digital engagement for a 360-degree view of the patient brings together these datasets into a single analytics engine providing insights for physicians and management staff to improve clinical outcomes and the patient experience.
Patient 360 Use Cases
Three sample use cases included in SpringML’s toolkit for Patient 360 include:
- Patient Journey: Research shows that almost half of the patients visiting a doctor do research and are aware of their condition, treatment options, and risks. A physician can be more effective and provide personalized care, if they get a quick glimpse into a patient’s journey over the last several months or years, and not just their recent medical history which is what EHR systems usually are limited to. This patient journey includes what medication a patient has taken and its effectiveness, how their behavior has changed, what plans, tasks and goals they’ve been assigned to, etc.
- Readmission: High readmission rates point to deficiencies inpatient care. There is also a financial impact owing to regulatory penalties under the Hospital Readmissions Reduction Program (HRRP). Our solution analyzes historical readmission rates to predict the probability of readmission for upcoming discharges and provides follow up intervention actions for high probability cases.
- Appointments: There’s a high opportunity cost for unfilled or canceled appointments and there’s no easy way for staff to see current and past appointments. Our solution provides an easy visualization to perform appointments with whitespace analysis and uses machine learning to predict which appointments are likely to be no-shows or cancellations. This allows staff to proactively reach out to patients.
To understand more about how providers can use technology and machine learning to optimize administration and serve patients watch the video.
SpringML’s Patient 360 analytics leverages advanced analytics and Machine Learning to bring key insights to providers, and helps them achieve their goal of providing high value, personalized care to their patients. If you still have questions, drop us a line at [email protected] or tweet us @springmlinc.