Technology has had a great impact in the medical field, revolutionizing how doctors and patients interact, how conditions are diagnosed, and the way surgical procedures are performed. However, the field of data analytics still faces many challenges in the healthcare sector, often lagging behind other industries when it comes to realizing ROI on data management solutions. This is due to significant barriers like information security, high volumes of data, complexities with governing access, and the high stakes nature of the industry.
Although these barriers are difficult to overcome, it is possible for healthcare organizations to find a solution that complies with regulations while gaining insight from information.
The Use of Data in Healthcare
There are two main areas where healthcare providers integrate data, starting with patient care. Data can help hospitals predict how many patients might be admitted and schedule staff members accordingly. Patients spend less time waiting to see a physician and physicians can budget their time properly, improving patient wait times.
In a practical use case example, analytics can be used to illuminate certain times of the year that healthcare organizations are likely to see higher numbers of appointments or admissions. For instance, October is national breast cancer awareness month. During this month, clinics see more patients for mammograms, perform more exams, and order more tests than usual. Data can predict how busy radiology departments will be and ensure an adequate level of staff is on hand.
Data analytics further supports patient care by facilitating practitioner performance evaluation through analytics. It allows organizations to gain a better overview of commonly over diagnosed or under diagnosed conditions, identify best practices, and continue improving the quality of patient care.
The second area most healthcare organizations employ analytics is in decision-making at the organizational level. Enterprises use data to gain departmental overviews, better visualize spending trends, and predict the resources or stock items needed. In practice, this might translate to better preparation for the flu season. By combining predictive analytics, population health statistics, and information from the CDC, clinics can prepare accordingly with supplies such as antibiotics, masks, and vaccines.
Challenges Facing Healthcare Analytics
One of the foremost challenges that healthcare organizations face is ensuring that information is kept private and secure. Patient information is highly sensitive and should be guarded with the utmost care. Mismanaged or breached data could put hospitals at risk for serious legal trouble. When searching for an analytics solution, organizations must be careful to identify solutions that protect information, comply with standards like HIPAA and other organizational benchmarks, or can be customized to meet these standards.
Additionally, these high privacy standards complicate governing access. Finding a balance between protecting sensitive information while still allowing data scientists, executives, physicians, and other professionals within the organization access is complex. This can cause further issues when patients switch healthcare providers. Data from the former network, like treatment information, may not translate well into the system of the new provider, causing inaccuracies like missing or duplicate information.
Another notable challenge in this subset of analytics pertains to the integration and management of healthcare data. Enterprises encounter large flows of information — processing and depositing information into specific databases. This large number of databases can be problematic because systems often aren’t designed with integration in mind. Enterprise resource planning systems (ERPs), laboratory information systems, radiology information systems, and enterprise master patient index (EMPIs) store information in different formats. Proper data management also involves updating data in a timely manner to maintain quality. When data is scattered across an enterprise or not updated regularly, the formation of a holistic patient view increases in difficulty.
The hesitation of healthcare organizations in fully adopting analytics tools also stems from the high-stakes nature of the industry. For example, if a hospital network adopts an AI-based solution that assists doctors as they diagnose patients, they must be certain that the algorithms used function correctly. Mistakes or algorithmic bias could lead to a misdiagnosis, additional charges from extra testing, unnecessary scheduling of surgeries, or, in a worst case scenario, cost patient lives. If a new software claims to deliver instant analytics results but cannot explain the algorithms used, healthcare enterprises should be wary.
Data warehouses are a great place to start. Warehouses allow healthcare organizations to consolidate data. While one seamless database for information is preferred, it is not plausible for every organization. Data warehouses can securely integrate information that is compatible. These warehouses are a great step for healthcare providers interested in consolidating data and gaining a full overview of patients.
Eliminating data silos is another option for healthcare enterprises. Data silos prevent providers from the full picture of business operations and patient information. Silos can exist across many departments or even within hospitals in the same provider network. If a patient visits another clinic or physician within the same network, that practitioner may encounter challenges in gaining access to the patient records.
If you do choose to migrate data to a new system or eliminate silos, establish firm key performance indicators (KPIs) ahead of time to gauge success. KPIs help monitor the process and success of a data migration of any magnitude. Suppose two hospital networks undergo a merger. Setting firm KPIs can help keep the migration on track and quantify the overall success.
If your healthcare organization is looking for new methods of managing or organizing data, our team at VanData is here to help! We understand the unique challenges that the healthcare industry faces. Our team specializes in data migration, data analytics, database management, and more. We would love to sit down with you and answer any questions you may have. Message us today and set up a free consultation.