Most of the interest (and venture capital funding) in healthcare AI is currently focused on very clinical use cases – automatically interpreting CT scans or retinal photographs for example, or trying to make a diagnosis from patients’ symptoms. These are the types of uses of AI that feature in a typical doctor-patient consultation. But behind each consultation is the whole multi-trillion dollar industry of healthcare: all the work, activity, people, and infrastructure that would perhaps be less obvious to patients (and hence data scientists and engineers turning an eye to using AI in healthcare) but which actually make up the bulk of healthcare activity and expenditure. Human resources, management, financial administration, logistics and supply chains, planning, laboratories, facilities, R&D, safety systems : all of these are data rich aspects of the healthcare industry where AI systems could find many uses.
I have a particular interest in using data to understand and improve the quality and safety of healthcare and I am struck by the wide number of uses that one particular approach to AI, machine learning, could have in the type of work I do. One of the challenges faced in making this happen is that the machine learning experts (by and large) know very little about the healthcare industry, and the healthcare experts (in turn) know very little about machine learning. Bridging this knowledge gap through collaboration is going to be key.
So what then are the types of problems in healthcare quality that could be addressed by machine learning? Here I outline, in an admittedly extremely broad and simplistic sense, the main types of problems that machine learning algorithms can be used to solve, and how they could be used to make the industry of healthcare better, cheaper and safer.
These are problems where the goal is to classify data into groups or categories. Examples include systems to help self-driving cars detect and avoid pedestrians or to automatically classify photographs according to subject matter (“Pictures of cats and dogs”)
- Classifying hospitals into categories of performance or service provision, to generate hospital quality ratings or scorecards
- Classifying patients into different categories based on diagnostic or procedure codes, or measures of healthcare utilization and cost (such as length of stay). These classifications are widely used as the currency in healthcare payment and reimbursement systems
These are problems where the goal is to make predictions based on an existing set of data. Examples include prediction systems used in the finance (e.g. Financial forecasting, fraud detection) and retail (e.g. More efficient logistics by predicting demand)
- Predicting the effects of a service reorganisation or a quality improvement intervention (e.g. What will happen if we introduce this new patient referral pathway?)
- Predicting patient outcomes for prognostication, providing better information for shared decision making or planning future health and social care needs
- Estimating case mix adjusted outcomes such as survival rates after cancer or rates of surgical complications. These case mix adjusted outcomes are often used to compare the quality of hospitals
- Predicting counterfactuals (what would have happened if the intervention had not taken place) as part of the evaluation of service reorganization or improvement interventions
- Predicting variation in demand for healthcare services
These are problems where the goal is to identify data points that are similar to each other. For example, clustering algorithms are widely used in recommender systems in online retail (“Customers who bought this item also bought these….”) and in entertainment platforms such as Netflix and Spotify
- Identifying inequalities in care provision and quality, according to time (e.g. the weekend effect), place (e.g. geographical disparities) and person (e.g. inequalities)
- Estimating the associations between processes of care and patient outcomes. These types of analyses are widely done as part of epidemiological or health services research studies and are useful in generating hypotheses for randomized controlled trials
- Grouping together similar healthcare providers to enable more representative benchmarking and comparisons (e.g. Between hospitals or between surgeons)
- Identifying subgroups of patients with unexpectedly poor outcomes. This could help in detecting safety problems
- Detecting significant patterns in time series data (Anomoly detection; also a Regression type problem). Time series such as Run Charts and the various flavor of Statistical Process Control chart are some of the most frequently used tools in healthcare quality improvement
This is the process of identifying the most significant variables (“features” in the language of ML) in datasets with lots of variables. These methods can use used to help summarise complex datasets
- Devise and select metrics to measure the quality and safety of healthcare systems
- Extract relevant information from electronic healthcare record systems with large numbers of data items
- Design datasets for programmes to measure the quality and safety of healthcare (e.g. Clinical registries and audits)
This is a very high level and simplistic look at the types of ML methods available – underneath this extremely broad (and arguably over simplistic classification) are a whole ecosystem of different methods and families of ML algorithms. The other key ingredient here is of course, data – without training data these algorithms are merely concepts. Healthcare is full of data, but using it for machine learning is going to throw up all sorts of technical and ethical challenges. More on this another time…