November 10, 2021 - by Parul Saini, Webmedy team
Advances in Artificial Intelligence-powered tools and solutions have transformed the healthcare space and optimized how patient care is delivered for improved outcomes while reducing costs.
Artificial Intelligence (AI) and Machine Learning(ML) are here and transforming the healthcare industry. AI has numerous advantages over the traditional method of analytics and decision-making techniques. The arrival of AI/ML and its associated abilities have created numerous opportunities for intelligent intervention.
Although several use cases and results of AI/ML projects are promising and exciting, the execution of these projects is still a challenge. The success of AI/ML depends on and is influenced by many internal and external factors. Internal factors like technical competency, mindset/cultural change, and the desire to adopt AI/ML project outcomes and external factors like sudden changes in the behavior of consumers and the desire to adopt AI/ML project outcomes affect the success of any AI/ML project.
Adapting to these changes begins with keeping an AI/ML project as simple as possible. It can be built upon and enhanced by adding new use cases and empirical data. Every project should start with defining out the expected results as S.M.A.R.T. objectives. S: specific, M: measurable, A: attainable, R: relevant, T: time-bound. Proper efforts should be made to review outcomes regularly and update them based on project progress and feedback.
Here we have listed some important steps that can help to achieve success on your next AI/ML project.
In this step, key points to look for are data types and formats. For cost predictions, the transactional claims dataset may be adequate, but for member sentiment analysis, initial data may be present in a raw, linguistic form. Also, some datasets like claims have analysis limitations since the initial purpose of dataset generation is for reimbursement only. However, a combination of right claims data with other clinical & social data will be able to provide excellent cost predictions. So it is worth it to spend time and effort to better analyze every perspective of the data available, before utilizing it.
It would be evident by now to categorize use cases to know if you are looking for a prediction in values, probabilities, or classification. Prediction in values can be a request of the claim amount, auto-adjudication rate, etc. Probabilities are usually best suited when there are multiple expected outcomes, like in the case of a prediction of maximum reimbursement for a rejected claim, before submitting an appeal. The predictions would get aligned with the models to be used, regression vs classification. Experimentations with multiple machine learning algorithms would be required after deciding on the right category and predictions.
Performance measures are quantifiable indicators to assess the effectiveness and efficiency of a process or business. They must be stated clearly and separately as compared to the performance measures of a related use case.
Performance measures are quantifiable indicators to measure the effectiveness and efficiency of a process or business. These must be defined clearly and separately as compared to the performance measures of a related use case. These are metrics for predictive models to define how accurate it is to predict the right outcome relative to fact. On the other hand, for use cases, measures can directly or indirectly depend on the model's performance. For example, for every 2% increase in prediction accuracy in the claims dataset, there is a 4% increase in cost saving. It is often recommended to separately describe both measures, model performance & use case performance.
As you progress towards achieving the best AI/ML solution, its success ultimately depends on acceptance by stakeholders. It is recommended to have at least one representation from each stakeholder group in an AI/ML project since inception. This will eliminate the chances of last-stage discoveries related to the use case or medium by which the results will be delivered. Once a use case and a corresponding solution are appropriately accepted, the next challenge would be to know the medium through which the AI/ML results will be communicated to the stakeholder(s). The right medium of communication would always be crucial so that the largest population of stakeholders can comfortably receive and utilize the results.
Once organizations achieve the desired results in their initiatives, continuous efforts are required to achieve future goals. As more and more stakeholders adopt and witness positive outcomes, the establishment of a feedback mechanism will improve the business values provided by AI/ML solutions. Improvement rate and stakeholder engagement in continuous improvement are key driving factors for success.
AI/ML has the potential to transform the healthcare industry. With proper data and assisted technology, patients and physicians can act together to create better treatment opportunities, and improved patient lives. The beginning of Machine Learning and its infinite sea of computing opportunities have inspired life sciences companies to adopt the fourth industrial revolution.
Leveraging AI/ML in Healthcare can be quite challenging as well as effort-intensive. But the advantages of implementing it are enormous and not to be missed in the 21st century.