As we know, a huge amount of data is generated across most industries and data has become a very useful resource in most organisations. In fact, access to high quality data and insight has become fundamental to achieving many of the strategic goals of any business. So, following on from my past series on data-centricity, I wanted to revisit this topic and examine data-centricity in healthcare – specifically, in hospitals.
Looking back to a past post on ‘Hospital Analytics – are we getting there’, I am sure you will agree there is no denying that advanced analytics is starting to play a key role in improving outcomes in some hospital and clinical environments. Given this, you would expect to see a strong strategic drive in the healthcare industry – a drive towards becoming more ‘data-centric’. However there is a very big difference between a few isolated analytical insights versus the entire hospital becoming ‘properly’ data-centric. In my opinion the drive towards data-centricity needs to happen quicker, especially if you consider what being data-centric will mean for a healthcare provider and more so if you consider the volume and variety of data being produced in the hospital environment. So, in this post, I want to explore why I feel hospitals as healthcare providers should become more data-centric.
Hospital analytics
In ‘hospital world’ analytics not only aids in improving patient outcomes and patient care, but it also assists in improving the financial processes, operational performance and the cost management of the healthcare provider.
Let’s look at just some examples, and the benefits of each:
- Through the use of data and analytics, healthcare providers are able to observe and better predict patient outcomes, in order to provide more improved or more appropriate patient care. Examples include predicting readmissions, predicting incidents such as falls and infections, queue analytics to decrease waiting times, collaborative and consultative recommendations for more successful treatments, and many more.
- Analytics can also be embedded in the software solutions used by hospital call centres – as analysis of individuals’ call detail records can help with patient information and the ability to provide quick and accurate support, all while keeping costs and waiting times down.
- With secure and necessary clinical information, doctors can also diagnose, administer treatment and prescribe medication more appropriately, based on a deeper and wider 360-degree view of the patient and his/her conditions, diagnoses and treatments.
Technology is changing lives and is creating a culture of instance gratification, as well as the ‘need and want’ for answers – also instantaneously. As a result, healthcare organisations find themselves having to become creative in how they aid patients quicker in order to deliver immediate attention at their clinic or hospital – and this process will need a data-centric approach to help generate actionable insight.
Data management
While the analysis of structured and unstructured data can help improve the expectations for better outcomes, you need to consider how to normalise and standardise formats and manage the quality of all data across a variety of data sources. For example, as part of master data management, you need to manage how gender, diagnosis and other code sets are handled. You need to manage how disparate datasets from a wide variety of data sources are going to be integrated across varying identification codes. Only through understanding the reporting, analysis and analytics requirements can you assist the organisation in defining the priorities and determine which data integration and which visualisation and statistics solutions are best for the task at hand.
Research data
Access to high quality data and insight is fundamental to a leading healthcare provider’s strategic goals, including practising specialised care and applying research outcomes by optimising the effectiveness of clinical trials, reducing the lead time from research to clinical practice and thereby improving quality and longevity of life. Undoubtedly, the quality and quantum of data is a fundamental element of delivering such insight.
For example, it is widely understood that Oncology is the field of Medicine in which evidence is being produced the fastest (it is said that keeping up with the literature can take a clinician up to 160 hours per week). This requires access to, governance of, and management of a large volume of ever-changing data, much of it unstructured. Instead of the more traditional “data extraction, movement and data integration” approach, the discovery, exploration and linking of research data and clinical outcomes with speed and agility requires a fundamentally different form of data integration that is designed for disruption. This in turn, necessitates a high degree of data-centricity to achieve with practical scaling while minimising cost and complexity.
Beyond these castle walls
There is a lot more useful data than merely what the hospital collects. Imagine how much better you can triage and diagnose a patient if you know he has had 3 visits to his GP and 4 presentations at the emergency department since his last visit to the hospital? Potentially at another hospital’s emergency department, that is.
With a more encompassing data-centricity, hospitals will be able to apply analytics and determine insights across medical records reachable across the whole country – and the longitudinal patient record – at any given time. When all the important information is eventually accessible through a single electronic console, health professionals will more easily be able to see what needs to be done for a patient, in line with what has already been done at many other places – all much quicker and more efficiently.
Earlier above I mentioned the 360-degree view of the patient. That is obviously the objective of the public electronic health record, but let’s face facts, that is still long in the making. In the meantime hospitals have to derive their own means to access to as much data about the patient as possible. Note that I used the word “access”, not necessarily “collect”. In a previous post I discussed the use of modern analytical platforms, but in order to do that efficiently, effectively and successfully, requires a data maturity and data-centricity not present in many hospitals today. The desire is there, sure, but I don’t yet see many fully data-centric data strategies being put together, never mind being implemented.
Security and privacy
All healthcare organisations will need to implement security measures to protect their data, structured and unstructured, as patient privacy is essential. Let’s be honest, if any real trust in data analysis is to be seen – then buy-in from patients, business and clinical users is paramount – and this often comes back to stringent security and privacy controls.
Concluding remarks
All health data directly reflects how we deliver care and medicine. It is inherently networked in nature and is constantly updated. Embracing the connectedness of this data, safely and effectively, will generate enormous clinical value. The significance of this is in its direct relevance to how a healthcare provider develops both a sophisticated data strategy and an enabling technology environment. Unlocking the complexity and embracing the connectedness of this data at an individual patient level is not simple, however embarking on a disruptive data-centric strategy opens up new capabilities to do so.
While to me it is clear that if any industry wants to see the value of data, this will mean making data a part of the day-to-day work. For the healthcare sector it means staff and patients understanding why they need to share more of their health data on a wider scale – as with more information more can be done. Encouragement to share, from management and senior level teams, will go a long way in championing this and help the transformation and impact, which data can have on any healthcare provider. However, this encouragement can only occur if the whole organisation embraces data-centricity, follows strict data governance and implements a mature and encompassing data strategy.
References
http://www.rbhitech.com/references/Applying%20BI%20to%20Healthcare.pdf
Memorial Sloan-Kettering Cancer Centre – IBM Watson Case Study
