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Data is a strategic asset, especially for healthcare organizations that generate enormous amounts of data and rely on it to make clinical and operational decisions. However, the lack of a strategic approach limits the data potential. At the same time, the value a healthcare institution can extract from it is massive and can contribute a lot to the organization’s growth, healthcare sector development, and patient care improvement.
A comprehensive data strategy can help a healthcare organization to understand better and manage its data. We have collected the main points you should know about healthcare data strategies and data analytics in healthcare to unlock the hidden power of data.
The Main Components of a Successful Healthcare Data Strategy
The amount of healthcare data is already huge, and it is growing exponentially day by day, making big data in healthcare never more relevant as now. Healthcare organizations operate dozens of data sources, like patient forms, lab results, electronic health records, medical images, wearables, etc. However, if this data is kept raw, in a chaotic order, scattered among so-called data silos, it will be pretty hard to put it to use, and it will probably stand idle aside.
So, your healthcare data strategy aims to make data usable and comprehensive for every member of your team and suitable for further application of technologies like artificial intelligence, machine learning, etc. Here are the five main components of a data strategy you need to focus on to achieve this.
Accuracy
The topic of accuracy is a big challenge for the healthcare industry, as errors and discrepancies are still very common, which decreases the efficiency of any algorithms used to improve decision-making or access new insights.
While the data fed to the algorithm is corrupted or damaged, you can not rely on any outcomes it provides. The implementation of unified systems of EHR is also at risk, as the inaccurate data will undermine any efforts to automate and digitalize processes.
The core of the problem is related to two main points: the inadequate usage of electronic health systems and data transfer between different systems and healthcare providers. While entering data, healthcare professionals might cut something or make a mistake and compromise the records. The same might happen when different providers exchange data.
Hence, when you are building a data strategy for your healthcare organization, you should understand the concern of data accuracy and design your data solutions in a way that would decrease the chances of errors happening.
Accessibility
Even the most accurate data won’t make any difference if healthcare providers can’t access it when needed. The lack of accessibility slows down the clinical workflow and makes patients wait longer for diagnosis and treatment, which might be crucial for patients in critical condition.
It is also relevant for cases when medical institutions need to transfer patient records between each other: often, it might take days, as many hospitals still use legacy systems and tools.
One of the main keys to data-driven health is accessibility, so break data silos, and make data easily available for doctors, so they will be able to dedicate their time to helping patients with their issues instead of spending the day trying to acquire or waiting for records or other necessary data.
Security and Privacy
Data security and privacy are the biggest concern for any industry but for healthcare organizations, it is a fundamental aspect of data strategy. Healthcare information is extremely sensitive and protected by compliance acts like HIPAA and HITECH. The security breach will not only lead to severe penalties but the loss of trust of the patients and community.
Prioritize data security and privacy in your strategy: encrypt data, implement a user permission model, constantly monitor accesses to detect suspicious activity, avoid public repositories, use 2FA, etc.
Scalability
The data size for every organization is only growing, and growing rapidly. If your original data strategy wasn’t designed to accommodate scaling volumes, you risk spending enormous amounts of money trying to find quick, momentary solutions to contain and preserve this data.
However, if you consider scaling in the very beginning and tailor your strategy to be flexible enough for these purposes, you will be able to choose appropriate storage tools and approaches to scaling up or down easily without losing extra budgets.
Automation
Automation of the data processes has a great benefit: you don’t need to rely on manual input, which means that you eliminate the majority of issues related to the human factor. The variety and capacities of data automation tools are wide, and you can choose or even develop a solution that will be able to cover your needs in this field and reduce the percentage of errors, time spent on operations, and costs required to maintain your data strategy.
Consider Implementing These Practices to Build a Strong Healthcare Data Strategy
While every healthcare organization operates and leverages data in its own way, there are still some common basics applicable to any institution aiming to build a successful healthcare data strategy.
Understand What Types of Data You Need and For What Purposes
The answers to these two main questions will help you to design a strategy that will deliver real value to your organization instead of a generic set of approaches and actions. If you understand the goals you are chasing, you can build a plan that will lead you to the desired outcomes.
It is also important for the strategy to determine the data types you will work with. The main type in use is structured data, but the need for unstructured data is also growing, e.g. radiology images or physician text notes.
Leverage the Capacities of the Cloud
Cloud opens new opportunities for healthcare institutions. The time of on-premise storage has come to the end: cloud enables flexibility, scalability, better control over expenses, and the capacity to leverage other emerging technologies.
When implemented the right way, the cloud can contribute a lot to resolving a lot of data-related challenges healthcare providers face. For example, the cloud provides real-time updates and access to the relevant data, meaning that doctors don’t need to wait hours or even days to get a hold of some parts of the patient record.
In the same fashion, the data exchange between different specialists or institutions becomes much easier as well. Real-time backups also provide an additional layer of dependability, as healthcare providers will be able to restore any data piece without experiencing any losses.
Using the cloud for data is also a more cost-efficient, flexible option than preserving on-site hardware to store all the data you need. In terms of scalability, in the world of constantly growing data, the cloud is definitely a preferable solution to on-premise storage.
Augment
Data strategy might be only the first step for implementing the aforementioned emerging technologies that can augment human input for better hospital management, diagnostics, and patient care.
Artificial intelligence and machine learning can be a great help to physicians to process vast amounts of data, and detecting patterns and overlaps that will make the diagnostics process much faster and more accurate than ever. AI combined with wearables can help doctors to care about remote patients and provides them with the same quality of services as regular, “on-site” patients.
And with the EHR data and family history, AI can predict the possibility of illnesses or conditions for a certain patient, which gives doctors an opportunity to take measures to prevent the disease or at least mitigate the risks.
However, again, everything depends on data: without precise data, it won’t be possible to train algorithms to a sufficient level of accuracy that will allow healthcare professionals to rely on AI. Hence, a consistent and thought-through data strategy is a must.
Focus on Patient Experience
Healthcare has become consumerized, and this brings another challenge healthcare providers should address: patients have strong expectations about the quality of services and care they want to receive. If they are not satisfied, patients are ready to switch providers to find the one that will be able to deliver the quality of experience they want.
The expectations concern every patient interaction with a healthcare facility, hence, providers should be ready to be heavily involved in the patient wellness journey.
To get a full overview of the patient experience, detect flaws in care quality, and remove them, healthcare institutions can leverage data analytics strategy to predict healthcare risks, take proactive preventive measures, offer personalized recommendations for patients’ treatment, measure the performance of practitioners to ensure quality, etc.
Consider this point for your data strategy to ensure that you are able to collect and process the necessary data to take advantage of data analytics value.
Educate Your Staff
Determining strategy and implementing necessary tools is only half of the way. You need to ensure that your staff operates data as intended, and it is all about skills and attitude.
Data literacy is a major skill your team should have, so you will be able to achieve the goals you are chasing, but unfortunately, only a small part of professionals are really competent to work with data reporting.
Hence, you need to educate healthcare professionals before exposing them to new strategies and tools. You need to explain the value and the reasons behind the new data strategy, as well as teach necessary data skills, so they won’t struggle to work with new systems and eventually reject and sabotage innovations.
However, it should not end with an initial one-time course: as the new strategy is being in action, ensure that more data-savvy employees help their less skilled colleagues. Set up other smaller educational sessions where everyone will feel comfortable asking questions they have encountered while actually using the new systems.
Still, hospitals and other medical institutions can benefit from AI capacities right now and improve the lives of clinicians and patients. Image recognition can lower the probability of human errors in medical records and help you to process some cases of medical images.
In Conclusion
Developing a data strategy for the healthcare institution demands skill and knowledge, as well as a vision of what your institution aims to achieve through data. Only then, you will be able to build a strategy that will be able to take your institution to a new level.
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Contact usFAQ
The data strategy in healthcare is built around the goal you want to achieve, hence, every strategy is custom and tailored for a certain organization. However, the main aspects making any data strategy efficient are data accuracy, accessibility, security, scalability, and automation.
The main three include patient medical records, hospital records, and medical exam results.
