Making sense of conservable days
1. Introduction 2. What are conservable days? 3. Why are conservable days important? 4. What are the main reasons for conservable day challenges? 5. Where can data analysis help? 6. Conclusion Introduction Conservable days represent one of hospital management’s most challenging metrics: everyone watches them, few understand them, and many struggle to improve them despite significant effort. Poor performance could indicate operational inefficiency, complex patient populations, data quality issues, or methodological mismatch, and usually some combination of all four. What are conservable days? Every inpatient visit is “coded” with standardized diagnoses and procedure codes. From these codes, an Expected Length …
Clinical AI in the new world of ChatGPT
For many years, we have developed niche AI to help our hospital clients. Two years ago, we released our first platform (CAC) powered by a metered (fee-for-use) external AI service. Specifically, we use Microsoft’s Cognitive Services platform and its Natural Language Processing (NLP) API to translate freeform clinical text into medical concept codes. This AI cannot be used for patient care, but it is very useful for other purposes including assistance in the medical coding process. At the time, this went against conventional approaches, as many industry peers were building proprietary NLP solutions to try to solve these problems. With …
Decision support for small community hospitals
Small community hospitals face different challenges than their larger counterparts, often lacking the financial resources to hire the technical expertise needed to make full use of their data. Nevertheless, they still have the same reporting and analytical needs as any other healthcare organization. While provincial and federal agencies do provide limited support based on standardized submission files sent to CIHI, Ontario Health, and other organizations, the same concern is often expressed: “The reports sent back to the hospital are not timely enough to act upon, and we don’t have the means to create the reports necessary to meet our hospital’s …
How to introduce Machine Learning into your hospital
Machine learning (ML) and Artificial Intelligence (AI) have consistently been the topic of healthcare articles citing advanced applications such as identifying malignant tumors from radiology images or automatically diagnosing patients. There is a very wide spectrum of uses for machine learning, many of which can be applied towards less captivating hospital operational purposes. In this article, I hope to illustrate that there are many hospital Decision Support (DS) problems that can be best solved using ML and that you do not require an advanced degree in mathematics to take advantage of the technology. We use Machine Learning extensively in our …
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