The digitization of healthcare and mHealth data automation are changing how we deliver care, manage mHealth analytics systems and share data in organizations. In particular, facilities now need to be able to share information between different departments and partners quickly and efficiently. That enables them to respond immediately in emergencies.
And the solution that allows successful data management within a healthcare organization lies in a sustainable healthcare data strategy.
Why Develop a Well-Functioning Healthcare Data Strategy?
After the push of the global pandemic in 2019, the active digitization of healthcare data started to grow exponentially. With nearly two zettabytes in 2019, healthcare data circulating in the industry has reached four zettabytes in 2022. That equals 4 billion terabytes:
And the amount of data will only grow further: it is expected to cross ten zettabytes by 2025.
Of course, large data volumes like these need to be appropriately managed. But how can organizations approach the management of data loads today and from now on? The answer is — with the right data strategy.
4 Best Practices to Include When Building Your mHealth Data Warehouse
By incorporating technology into your data strategy, you can stay ahead of competitors and provide more efficient patient care. Here are the four practices to implement in a healthcare organization to develop a great data strategy.
1. Data Augmentation & Automation
One way to approach data management is by using ML algorithms to augment human intelligence and switching to automation. ML allows computers to learn from past data-driven experiences without being explicitly programmed for each task.
That translates into machines being able to adapt and assist instead of relying solely on human intervention, which is especially critical when something goes wrong. Also, this improves model prediction accuracy for decision-making and reduces data collection and labeling costs.
Similarly, using automated technology like chatbots, patients can interact directly with their doctors via text or voice messaging. The data is then processed automatically while AI defines how to proceed with the patient. That allows more patients to receive attention and decrease wait times at the doctor’s office — a massive win for both parties.
Eventually, with less time spent on administrative tasks like managing permissions or logging data and fixing entry errors, staff members can focus on providing patient care services in the first place.
2. Moving to the Cloud
A cloud is a powerful tool; moving to it can help your healthcare organization in many ways. Cloud-based solutions are more flexible, reliable, secure, scalable and cost-effective than traditional on-site systems. Besides, cloud services and records stored in the cloud can be accessed from anywhere, so organizations may not worry about losing access because of a power outage or other circumstances.
In addition, moving to the cloud will save the time spent on administrative tasks because you can have them automated. For example, automated patient record upload and real-time synchronization across all departments will eliminate the need to perform these tasks manually.
3. Strong Data Governance Structure
Data governance establishes policies, procedures and protocols to ensure that an enterprise can control its data assets. Also, it helps keep data of good quality while providing a framework for handling compliance requirements.
Data governance should guide how resources are allocated across different organizational functions. For example, it can help determine who has access to which types of information at any given time. It should also help identify potential risk areas within the organization and provide recommendations for mitigating those risks.
Without guidelines outlining how data should be approached and managed, there is a lot of space for confusion, errors and chaos for medical personnel working with healthcare data. Therefore, a clear data governance structure provides visibility regarding what is happening with the organization’s mHealth analytics systems.
In turn, this allows fixing problems related to data management before they become too complicated or costly. Besides security threats, this includes issues with billing, data entry, filling in electronic health records and other related processes.
4. Staff’s Data Literacy Training
Data literacy helps users understand what they can and cannot do with healthcare data. In particular, it helps learn how and why data is collected and used within the healthcare system before they can use it effectively. At the same time, data literacy allows for learning to identify when something is wrong to fix the issue in time and prevent it in the future — whether it is an error in electronic health records or how patients are treated on overbooked days.
Data literacy is also a mindset. It is about understanding how clinical data storage improvement facilitates better clinical patient support systems and applications and, overall, better care. That is why educating medical staff on how to work with data before they start is essential.
You can upskill your medical staff’s data literacy by helping them understand the importance of data, how clinical data storage improvement leads to better clinical patient support systems and patient experience, and why the latter is vital in healthcare.
While there are many resources online, you can also hire experts to provide professional training to your personnel to educate them on how to operate data in your organization.
Healthcare Analytics Strategies to Use in Healthcare Data Strategy Development
A thriving healthcare data strategy covers setting clear goals and ensuring everyone involved in the project understands them. With the tips below, you can build a robust data structure that will help your organization be more efficient when managing clinical, patient and administrative data.
Identify Goals and Objectives
Identifying your goals and objectives is one of the first steps in developing a data strategy for your healthcare organization. While this may seem obvious, it is easy to lose sight of why you are collecting data in the first place and how you will manage it.
The crux is that you need help defining the problem to solve with the strategy you are building to develop an effective solution. So start with identifying challenges or pain points within your organization and finding ways to address them through various approaches to data collection.
Eventually, you should be able to articulate precisely what problem, or problems, need to be solved when speaking with stakeholders within your organization. That is because, in most cases, they will be responsible for implementing any solutions this new strategy provides.
Assess Your Existing Data Infrastructure
After learning the weak points in your organization, it is time to evaluate what is working well. So the next step in designing your data strategy is to assess your existing data infrastructure and mHealth analytics systems. That involves understanding the current state of your data, including how it is being collected, used and stored. You should also be able to identify any gaps or issues that may exist within your current system.
So to get a complete image of how data flows within your organization, try to think of the following:
- how the data was collected (e.g., via manual entry or automatically)
- the quality of the data (e.g., accuracy and completeness)
- why the data was collected (e.g., because of a regulatory compliance requirement)
- how long can you retain the data you collect
Finding answers to the above and diving deeper into your existing data infrastructure will help you understand what you lack in your current system and what you need for healthcare and clinical data storage improvement.
Solve One Problem at a Time
Now that you have determined where you are and want to be, you can start thinking of how you can get to the destination. In other words, begin looking for solutions to the problems you have identified to achieve your objectives.
A crucial thing to consider at this stage of your data strategy development is that you should target only some areas at a time. In other cases, that will be too demanding and not efficient.
Thus, think of what issues are the most urgent and which you may deal with for some more time. Or, focus on the minor problems first to get them out of your way and move toward complex issues that require a more drastic change.
Establish a Clear Line of Communication With Stakeholders
The key to a successful data strategy is ensuring that it meets the needs of all stakeholders. Healthcare organizations should establish a clear line of communication with the people whose data is being collected, used and stored.
Stakeholders should participate in the data strategy process to have a say on what data is collected, how it is used and how long it will be retained. The stakeholder’s voices should also be heard as the strategy moves into implementation because they know their business better than anyone else.
So after you establish clear goals and objectives, as well as strengths and weaknesses of your current data system, make sure that they are clear to the stakeholders and align with theirs.
Then, it is time to start designing detailed solutions to be implemented as a part of your strategy. You can now start bringing your view to reality to provide better clinical patient support systems.
Data is an integral part of a healthcare organization, and it is crucial to treat it as such. Without a quality data strategy, the potential for improved care and more efficient operations within your healthcare organization is limited. If you are considering building your healthcare data strategy, contact us, and we will design a project plan for you.