Big & Small Data In Hospitals

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During the past two decades hospitals have invested large sums in advanced IT systems. Not many of them, however, use the IT potential to increase the quality of treatments, co-ordinate care and manage organisational processes. How can we change that ? Build a data management strategy.

Since healthcare providers introduced IT systems the amount of data gathered and processed has been rapidly increasing from year to year. Thanks to electronic medical records and digitisation of organisational departments we now know more about patients and how hospitals work. At least in theory, because having information in a database is one thing, but their proper use is another story altogether. Even though hospitals acquire resources to buy hardware and software, sometimes with great effort, they rarely try to maximise the return from their investment in IT in terms of finances and quality.

According to research conducted last year by Dimensional Insight, more than half of American hospitals lack a strategy for data management and analysis. There are many reasons for neglecting data analysis: limited staff dedicated to information processing, more important priorities, lack of time required to implement a strategy, additional costs, belief that the data is of low quality or lack of pertaining standards and knowledge. Even private healthcare providers, which are forced to maintain financial feasibility, do not know how to use IT resources to increase quality and optimise costs, instead focusing on predefined reporting.

Hospitals which based their operational strategy on data management enjoy many benefits of this approach. These include an increase of trust in digitisation and data among employees, higher acceptance for using eHealth tools, increased standards of care and treatment, increase of treatment safety or lowered operational costs. These effects can be noticed even on the clinical layer of their functioning. A data management strategy is also the basis for other initiatives, such as population healthcare management. Here are some other benefits created by a well prepared information flow system: reduction of administrative costs, more fluent patient admission, better medical staff working conditions, limitation of misuse, better co-ordination of patient treatment processes, clinical decision making assistance, and lowering of medical intervention costs (e.g. repeating the same medical tests).

Information Structure

Even the best strategy won’t help, if organisational attitudes towards digitisation do not change. Creating an innovative culture may prove to be the hardest step of this process. Enthusiasm for innovative approaches should trickle down from the hospital management and department managers. It should be based on the understanding of the need for information. An analysis of key organisational issues and using IT resources in solving them helps with this phase. For example, increasing healthcare quality is very often limited by a lack of quick, real-time access to data. If a doctor has to wait for test results and a nurse does not see the information recorded by the doctor moments ago, this can create data obstacles which lead to major healthcare flaws. The identification of issues and using IT to eliminate them increase the acceptance level for the digitisation process as well. This way the staff understands the aim of the digitisation. They will know that the purchase of computers and systems provided concrete benefits. When trying to include the whole staff in the digitisation process, we should try to find leaders of change at each organisational level. There will surely be enthusiasts of new technologies, which can serve as a source of inspiration and motivation for others.

Data standardisation and interoperability are yet another major challenge, especially when hospitals use different IT systems in each organisational departments (electronic medical records, hospital pharmacy, laboratory, administrative departments, medical imaging, etc.). Ideally, a hospital should use a cohesive database, which can be used to perform comparative analyses. If this is not possible, the organisation should introduce interoperability for all systems as fast as possible. This is a tedious process that takes years, but from a long-term perspective it is the only way to get rid of an IT mess. Business intelligence systems are especially useful when conducting more advanced analyses, even in the case of different data sources.

A culture of transparency and engagement is the third foundation of an IT strategy. It is the medical staff that is often blamed for the low quality of treatments, long registration queues or medical errors. In most cases the blame lies elsewhere – that is with poorly organised processes. In a transparently managed hospital it is the role of ground level employees to identify and solve everyday obstacles, without involving managers and directors. This strategy is used, for example, in the automotive or aeronautics industries, where it has proven to work well, especially from the perspective of proper staff motivation.

Implementing an effective strategy

You can extract data and reports from databases in order to inform better decision making processes, even in the case of hospitals with the lowest technological development levels. Simple analytical modules introduced into an integrated system enable hospitals to prepare multidimensional comparisons and monitor processes, becoming sources of knowledge about what is happening inside an organisation. Reporting, which is often undervalued and inappropriately used, belongs to the lowest level of data management systems. Many types of information require correlation and deep analysis. An increasing number of patients can be a result of a better hospital image among the population as well as of a changing epidemiological situation, or of both these factors. Instead of limiting oneself to standard comparisons, it is better to think through which data will help perfect the organisational and clinical processes, as well as assist the achievement of goals. Available knowledge should be used to draw actual conclusions.In the case of a higher data management level we use real-time monitoring – the available information gives a picture of the present situation. These types of data inform, for example, clinical decisions.

Checking interactions between the medicine that is being prescribed at the moment and the one the patient is taking or used to take is the best example of this process. This also includes data transferred from the medical archive to the laboratory or the data gathered with measuring devices (also in the case of telemedicine solutions). The next step of the strategy should include evaluation processes. If an abnormality is identified, what was its cause and how can it be prevented in the future? Heads of wards can analyse clinical processes or complications, doctors can browse patient data in order to assess their health condition and identify epidemiological threats in the hospital, and administrative employees are able to view financial balances in order to monitor budget performance. Quality control teams, which include doctors, nurses, quality department representatives and directors, can use the data to assess the present state of strategy and plan execution.

Yet even on the level of data evaluation we only react to what has already happened, trying to mitigate mistakes. This is why the prediction step is the highest layer of a data management strategy. Based on previous data, a hospital can perform a simulation of its functioning (cost and income development) and potential risks. On the clinical level this includes a prognosis of healthcare conditions on the basis of information gathered in an electronic archive. This way patients become part of a preventive programme, which tries to proactively protect them from potential illnesses. With the development of artificial intelligence systems, which analyse large amounts of data, the point of focus of medicine will shift to prevention, so this level of information management will become crucial.

Investment and innovation are required – not an impossible task
Building an organisation focused on the use of data to systematically improve internal processes is not an easy task. It requires an innovative approach to patient care as well as an investment in both technologies and people. This task, however, is not impossible. Neither does it require exceptional competencies or time and resources. At the first step, healthcare providers should focus on several tasks:

  • development of a strategy to integrate data in the framework, including all the sources, e.g. other subsystems, medical devices;
  • data use for operational, clinical and managing purposes as well as sharing the analyses with other entities, for example local authorities, in order to develop joint population healthcare strategies;
  • creation of data protection strategies and their constant updating processes;
  • co-operation in interdisciplinary teams with the goal of solving issues, including IT department employees in clinicians’ meetings and quality strategy development;
  • defining the so-called golden data sources, i.e. those providing high quality clinical and organisational data with the highest precision and their use in analyses;
  • creation of data infrastructure for the hospital – defining the types of data gathered (which are gathered at the moment, which should be gathered) and their analytical potential;
  • creation of decision making teams in scope of quality, development, and management (with the use of present staff). Their aim is to analyse data and define requirements for IT resources (instead of imposing decisions); and
  • development of strategies which place the patient at the centre of processes, e.g. healthcare co-ordination, increasing hospital stay satisfaction, remote care. Using IT systems to achieve those goals.

Data is a key resource for each healthcare facility. It provides information about internal issues, expands knowledge, allows us to identify new medical and organisational capabilities, and reveals the weakest links in the work flow, but most importantly it is indispensable for the development and improvement of processes. In order to use data properly we need to organise it and then build a work culture based on information.

The article was published on HIMSS Insigts Blog.

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