There is a strong need to increase the efficiency of healthcare workflows, to improve the patient experience and optimize the usage of healthcare resources. One way to achieve this goal is to use Machine Learning techniques to predict how long a certain surgery will take. This project is a collaboration between Cambio Healthcare Systems (one of Scandinavia’s leading suppliers of healthcare information systems and a growing player in the European market with just over 100,000 users across general and university hospitals, specialist units and outpatient units), Capio S:t Görans Hospital and KTH Royal Institute of Technology.
Anonymized data will be provided by Capio S:t Görans Hospital to address the challenging problem of predicting the surgery duration for patients. Current algorithms are often based on e.g. calculating an average of the surgery times of recent surgeries for a certain procedure code and surgeon/team. The main research question to assess is whether it is possible to construct an ML model which is better than the current algorithms. Another question to assess is whether the hospital staff finds the ML model useful from the perspective of accuracy, interpretability, etc.
A number of variables from anonymized surgeon/team history as well as anonymized patient records will be available, including age, anesthesia form, physical status (ASA) classification, vital parameters, lab info, earlier diagnoses, procedures, medications, etc. In addition to the main research question, the student can also address questions such as:
• Are there certain patient groups (e.g. certain diagnoses / procedures) which are particularly interesting to analyze?
• Which criteria are important for evaluating the model (accuracy, interpretability, complexity, compute time, etc)?
• Which features are most important for the model?
• Which type of hospital staff would find the model useful, and which requirements would they have on the solution?
The project is important for the Swedish healthcare system, since there is a general lack of hospital staff/beds and healthcare providers are struggling to give all patients sufficient care while keeping down the costs of healthcare resources and staff. If accurate surgery duration predictions can be done, hospital resources can be better utilized and actions can be better planned. It is expected that a machine learning approach has strong potential to provide a promising data-driven solution, which could be scaled up and tested in real-world healthcare scenarios. It remains to be seen whether the resulting inference model can combine satisfactory prediction accuracy with interpretability to ensure that doctors and nurses understand the basis and feel confident/comfortable about the prediction.
The student will be supervised by Cambio Healthcare Systems, who will guide to the Cambio COSMIC data structure, and also help the student to reflect over how the model could eventually be used as part of the existing healthcare information systems. Workshops will be conducted together with Capio S:t Göran to ensure that the model is built on correct assumptions and solves relevant healthcare problems. One project goal is to be able to evaluate the model performance together with Capio S:t Göran staff. For questions about the project, contact email@example.com.
Within 6 years, the Swedish government wants to be the best in the world in E-health. At Cambio Healthcare Systems we fully support that ambition. Cambio is a leading IT company in the field of E-health.
Cambio was founded in 1993 in Linköping by two engineers with the ambition to digitizing healthcare. Today Cambio is an international company with headquarters in Stockholm, development centers in Linköping, Motala and in Sri Lanka, offices in Denmark and England. We are 600+ employees with different backgrounds, areas of expertise and tasks, but with a common goal to create world class IT solutions that contribute to the greater good within the E-health industry.
We look forward to receiving your application!
Last day for applying: 31st of August 2019
The selection of candidates will take place continuously, therefore please apply as soon as possible.
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