6 Principles from Antimicrobial Stewardship Programs that can be Applied to Enhance the Effectiveness of Inpatient Consults in Behavioral Health
In the same way antimicrobial stewardship programs (ASP)is being proactive and reaching out to providers regarding antimicrobials, behavioral health providers need to do the same. Current practice for many providers is to wait for consult requests. However, there are patients that can and will benefit from a consultation with a behavioral health specialist. The behavioral health and addiction medicine community can gain several valuable lessons when it comes to providing unsolicited inpatient consultations.
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Just Say No to Notes!! Empower Providers by Modernizing Data Entry in Behavioral Health
Modernizing data entry in psychiatry, and broader behavioral health programs, can help providers improve patient care and outcomes, increase revenue, and optimize resource allocation. The consistent use of unstructured notes in behavioral health limits the ability to gain downstream value from the data providers enter. Enhanced analytics, made possible by modern data entry methods, provides detailed and accurate insights into patient behavior, treatment outcomes, resource utilization, and areas for opportunity.
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Tableau Killed The Healthcare Data Analyst: 5 Ways Analysts Can Fight Back
Dashboards and visualization tools have led organizations to assume that anyone can be an analyst. Tools like Tableau can be useful in quickly exploring and presenting data, but skilled analysts are still necessary to perform more complex data analysis tasks, provide context and expertise, and help organizations make informed decisions based on the insights derived from the data. Healthcare organizations should heavily invest in analysts that are focused on analyzing data, instead of just focusing on fulfilling data requests for non-analysts. Data is healthcare’s more expensive asset; analysts can maximize your data’s value.
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9 Ways Machine Learning & AI Can Measure and Improve Outcomes for Addiction Medicine
Using latest in machine learning and AI, Shepard Health enable doctors to provide personalized care while they consider an array of factors that could determine what successful treatment looks like for each patient These technologies have the potential to revolutionize the way outcomes are measured for addiction medicine treatment by providing a more comprehensive understanding than simple sobriety alone.
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Utilizing Machine Learning, Predictive Modeling, and Dashboards for the Real-Time Identification of Patients Most Likely to Leave Substance Use Disorder Inpatient Treatment AMA
Adherence to treatment for patients diagnosed with substance use disorder (SUD) is a constant issue. Patients with SUD are at an increased risk of discharging against medical advice (AMA) as compared to patients without SUD.1AMA discharges correlate with increases in thirty-day mortality rates, and cause an increased variance in bed occupation which raises costs and is detrimental to revenue Shepard Health hypothesizes that a predictive model can be developed to identify patients at higher risk for leaving AMA and the associative causes for this course of action.
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