Tag Archives: data analysis

Last November, the AQuAS played an important role by means of the PADRIS programme in the Critical Care Data analysis Summit and Tarragona Datathon 2018. Talking about it has been in the pipeline since then.

The PADRIS programme contributes to the improvement of people’s health by making anonymised health information available for re-use by researchers in Catalan research centres, in compliance with the current legal framework and established principles.

Is this seen in practice based on a professional’s experience?

Today we interview Maria Bodi (@mariabodi23), doctor of the Intensive Care Medicine service in the Hospital Universitari de Tarragona Joan XXII, expert in clinical management and aspects of quality and safety in healthcare. Like many health professionals, she combines healthcare practice with research.

What is your day to day like?

As head of the Intensive Care Medicine Service at the hospital, in my day to day my basic task focuses on managing the service and organising the care for critical patients by coordinating the work of the professionals involved. More than 150 people work in the service including medical professionals (specialists in intensive medicine, resident intern doctors), nurses, nurse aids, ancillary staff, physiotherapists and secretaries. In addition, it is a service which participates and collaborates in the teaching of medical, nursing and physiotherapy degrees or certifications.

I try to facilitate the participation of professionals in the strategy of the service. This requires articulating and coordinating all efforts made with a clear objective in mind, which is providing quality care to our patients. To a greater or lesser degree, it is necessary to encourage and coordinate the participation of professionals in care-giving, management, teaching and research. This will guarantee the commitment of a worker in the service’s strategy and its organisation.

If we focus on medical professionals, each member of the team is in charge of a specific area of our speciality and we therefore provide ongoing training for the entire team, assess results and commit ourselves to carrying out actions which derive from the analysis of our results.

What do you think about the format that was used for the Datathon?

The Datathon was the result of a series of developments over recent years within the field of secondary use of data in patients’ clinical records for management and for research at a top level. The experience was very good. Pure science, with doctors, technicians and technology all at the service of real-life data analysis in order to find the best scientific evidence.

In the last three years, our group has delved deeper into the study of data and also into the assessment of the quality and safety of data for secondary use. Our progress has allowed us to collaborate with other leading teams such as the team of Dr Leo Celi of the Massachusetts Institute of Technology with whom we organised this event.

In your opinion, how can the quality of care be improved in intensive care units?

We have to aim for excellence. We need to tackle all the dimensions regarding quality of care. We need to improve effectiveness, safety and efficiency. But when talking about good and efficient results, we are not referring to the number of actions done at a particular cost. We are talking about bringing value to the patient, to the work team, the organisation, the health system and society. How is this achieved?

Our team has worked on developing a methodology which enables us to have automatic indicators of quality. This has been possible because all of a patient’s bedside devices (mechanical ventilation, monitoring, dialysis machines, etc.… ) are connected to the system of clinical information and this information, from the hospital’s clinical records, from laboratories and imaging tests, is integrated. This information is incorporated by professionals in an ordered manner.

By means of these indicators designed with innovative technology, it is possible to assess the processes of care and their results. If the professional participates in the design of a process of care, in its planning, and knows how things are being done and what results there are, they get involved and commit themselves to the aims of the service and the organisation.

Likewise, one needs to move from a reactive to predictive medicine, preventive and personalised. We have data with which to start working along these lines. Nevertheless, the scale and complexity of this data makes it difficult for methods of artificial intelligence to easily translate it into relevant clinical models. The application of cutting-edge predictive methods and data manipulation require collaborative skills between professionals who are medical and technology experts, as well as new models in the treatment and analysis of data.

We have read that it is possible to assess risk in an intensive care unit in real time. It seems difficult to imagine this. What can you tell us about it?

It can indeed. We work on the assumption that we have access to the data stored from all patients that have been admitted to an ICU, or in more than one ICU. If the combination of a group of variables (demographic, clinical, laboratory results) leads to a complication or an adverse event, a trained computer model using this data can predict the risk of the same complication or event occurring if it detects the combination of this group of variables. This is the basis of predictive medicine.

Understood, but in practical terms, what is purpose of assessing the possibility of a risk?

It can range anywhere from analysing a risk or predicting the appearance of a complication in the course of a disease, a problem of safety, an adverse event, the need for or an increase in dosage of a specific drug, to a specific therapy. It can predict the probability of an improvement or worsening and even the risk of death for a patient as a result of their disease.

Medicine already has calculators to assess a risk of death due to a disease based on data bases created by professionals using manual registers. But these days, with the automation of data registration- an example of this being the Clinical Information Systems in our ICUs – today’s methodology based on big data and artificial intelligence allows us to have much more detail when assessing risks.

When there is talk of moving towards a health model based on value, what does this mean exactly?

It is about organising work around the specific conditions of a patient which optimises their cure. It is a question of paying organisations and hospitals for the value they bring. Their results and their costs are the key components of the value which a health system and its professionals offer patients. But if we are talking about results, we are not only interested in whether a patient survives the ICU or not. Results are measured in terms of quality, their capacity of going back to their normal lives, their jobs, etc…

Therefore, to push efforts for improvement, we need to base ourselves on multidisciplinary work and a health model based on value; this means changing an organisation’s business model and investing in systems of measurement, analysis of clinical results and costs.

What importance do you attribute to the fact that data is obtained automatically and not manually?

It means data can be analysed using one source only, errors are minimised and a professional’s time is not required for the task of inputting the data.

How is the data included automatically?

In the ICU, clinical information systems now enable all information to be integrated. Apart from the data which professionals input in an ordered manner during the process of care of a critical patient, laboratory results, imaging tests, clinical records and the data from all a patient’s bedside devices are integrated too (mechanical ventilation, monitoring, dialysis machines, etc…).

What does “secondary use of data” mean?

Primary use is that which is used on a day to day basis, at the patient’s bedside, to take decisions relating to diagnosis, treatment and the planning of the process of care. For example, a decision is made whether to increase the dosage of a drug based on laboratory results.

We talk about secondary use when we refer to using data for management or for research. Obviously, the end aim is still the improvement of care for a patient.

 (To be continued …)