Unlocking the power and potential of data-enabled healthcare

How to leverage health data

When various different models of care are available (e.g. face-to-face, online video, phone), patients, with the support of their clinical team, should be able to select their preferred channel. People will be much more likely to opt for new healthcare models when the experience is intuitive, trusted and simple. 

It is, therefore, vital to put clinicians and patients at the heart of any attempts to improve data. Having a clear view of patient journeys (across multiple health providers) can help identify opportunities to: 

• Capture data: The aim here is to record accurate, current, trusted data at source – irrespective of whether that source is analogue or digital. This could be both health data as well as health relevant data entered by health providers, administrators, as well as data entered by patients themselves.

• Retain data: The aim here is to ingest and store data in a way that is secure and yet also accessible to health providers. To ensure clinicians can maximise its value, data needs to be presented in a simple way that can be easily understood and analysed.

• Share data: The value of data can be multiplied when it is shared or integrated with other health providers. Furthermore, if data is accessible to patients themselves, this can empower them to self-regulate their behaviour and be proactive about their treatment and prevention.

• Mobilise data: Perhaps most important of all, viewing the above three steps (capture, retention and sharing) as the launchpad to translate data into meaningful information and insights that can be actioned to improve health services and patient outcomes.

To effectively capture, retain, share and mobilise data, health providers need a comprehensive strategy that encompasses: 

1. Standards, governance, and interoperability
2. Privacy and security
3. Monitoring and evaluating outcomes.
   

Let’s explore each of these strategic priorities, in turn.

1. Standards, governance, and interoperability

With virtual health models still in their nascent stages, now is the perfect time to establish rigorous standards, governance, and interoperability.

In its rawest form, a lot of virtual health data is unstructured. Standardisation allows health organisations to process unstructured data (that is stored in different places and formats) and combine it with structured data for analysis. 

For example, natural language processing, information retrieval and machine-learning techniques can convert unstructured free-text data into computable data for AI applications. The Australian e-Health Research Centre is partnering with Queensland Health to extract information from the free-text contents of cancer pathology reports. This allows a real-time cancer registry to process pathology reports daily to provide cancer incidence data to inform activities such as cancer monitoring, health service planning and research.

To convert unstructured data into meaningful information, healthcare providers also require governance practices to underpin data recording, optimisation, and analysis. Governance should extend across the organisation itself and also encompass data-sharing agreements with third parties. Therefore, governance needs to be flexible enough to adapt data and analytics to different healthcare scenarios. 

One essential pillar of good data governance is privacy and security. It's important to embed this early in the design phases of new health models, rather than making it an afterthought that is (expensively) retrofitted later on. Mapping entire patient and clinician journeys can help identify where data needs to be exchanged or combined with different parties, and in what format. At each point, security and privacy requirements should be specified. 

Another essential pillar of good data governance is interoperability; establishing connections between systems to allow the secure, timely, accurate exchange of data. When this is managed well, health data and health relevant data can be combined from various sources (e.g. virtual health interactions, electronic health records, patient case histories) and activity and lifestyle data (e.g. real-time digital data from sensors, wearable devices and trackers).

To enable interoperability, health organisations should identify silos and barriers to sharing data, and then seek ways to remove these. This can pave the way to adopt digital tools that empower patients to be more proactive about their own health.

There is also the potential for health organisations to go beyond the minimum regulatory requirements of Health Level 7 standards to implement FHIR (Fast Healthcare Interoperability Resource) data interoperability to support risk management strategies and security of data formats. Here, there is an opportunity for health leaders to work with their industry bodies to establish interoperability standards that provide common security and privacy requirements.

2. Privacy and security

Activating  virtual health, in particular the use of remote patient monitoring and asynchronous communication, increases the surface area an organisation must defend from a cybersecurity perspective. It’s therefore important to assess whether the organisation’s existing technologies and procedures can handle the increased and varied scope of devices and threats.

Assessments should review current data protection policies and security program controls to establish whether they are sufficient to cover the more complex network boundary and architecture brought on by virtual health (not to mention the full ‘reach’ of data interactions with third parties). 

It is important to look ahead and consider the organisation’s long-term goals for virtual health expansion. This helps clarify what security and privacy capabilities will be required in future, and therefore what measures should be established now in readiness for this (e.g. data interoperability, tracking and interventions, device integration, cyber threat mitigation).

Broadly, good security and privacy processes for virtual health include:

• Security assessments: Testing the efficacy of the security controls protecting health information (using a variety of tests such as vulnerability assessment, penetration testing, security configuration review, etc).

• Digital identity: Establishing the security discipline to enable the right individuals to access the right resources at the right times for the right reasons. 

• Monitoring: Clarifying the required capabilities to construct, collect, aggregate, and correlate cyber risks and events.

3. Monitoring and evaluating outcomes

The beauty of virtual health models is that – with the right preparation – they can evolve and flex in line with changes in consumer behaviour and clinical results. In this way, service delivery and patient outcomes can continually improve.

To enable this, it’s essential to understand how results will be measured. While virtual health is in its (relative) infancy, now is the time to collect baseline outcomes to compare against future outcomes. Longitudinal data can reveal patterns and trends that can inform further improvements.

To determine what data matters most, consider this from the clinicians’ perspective: What data could best support their decision-making? And how might findings be presented to clinicians to support their decisions on ‘best practice’ given a patient’s characteristics (e.g. visualisations, dashboards)? It’s also important not to overlook the possibility that – if clinicians know certain metrics are being calculated – this might affect their behaviour when recording certain data.

In this design phase, it’s vital to closely consult with clinicians. Health professionals understand the basic logic that strong evidence generally delivers stronger outcomes for patients. Similarly, the stronger that data (i.e. evidence) is managed, the better that health leaders can assess best practice and optimise delivery models to achieve higher quality clinical (value-based) care. 

Of course, the outcomes of health service delivery extend beyond the immediate experiences of patients and clinicians too. So, in clarifying what data to measure, it’s also worth considering how to track economic and social participation, and improved quality of life.

Having clarified what outcomes will be measured, health organisations can establish the methodologies to collect data, calculate metrics, and present outcomes.