Read about the challenges healthcare data analytics software faces today and their solutions.
Data analytics is crucial for any industry. But it is vital for healthcare. Every day, the healthcare industry collects and generates large volumes of data on patient vital signs, treatment plans, medical tests, and prognoses. Healthcare big data is growing so fast that it is projected to reach a compound annual growth rate of thirty-six percent by 2025. By comparison, the volume of data in manufacturing will reach thirty percent, in financial services twenty-six percent, and in media and entertainment twenty-five percent. Indeed, healthcare is ahead of them.
What is Big Data Analytics?
Big data analytics software collects, interprets, and processes large and complex data sets to find valuable insights. Analysts extract trends and patterns from analytics reports and send them to the stakeholders who can make informed decisions based on these reports.
There are four types of big data analytics in healthcare.
Descriptive analytics helps find out what happened and when. It analyzes real-time and historical data and identifies trends in them. For example, analytics tools found a patient has been missing frequent doctor appointments.
Diagnostic analytics shows why a particular event occurred. Analytics algorithms compare the detected trends to find cause-and-effect relationships between them. In the past, there have been cases where patients missed an appointment. It happened because the unit clerk who usually calls patients and reminds them about the visit took sick leave.
Predictive analytics forecasts opportunities and assesses risks for more accurate and effective business decisions. The patient can choose another provider and medical center where they will receive timely reminders about the upcoming visit. So, it needs to be prevented.
Prescriptive analytics determines specific actions to optimize processes and avoid risks. The company can set up an automatic patient notification system via SMS or bot calls.
Key Challenges in Healthcare Data Analytics
Data Collection
Every healthcare organization wants to collect clean, accurate, complete, and properly formatted data. Such data can be combined easily to find valuable insights and solutions.
Healthcare data can be structured (statistics, barcodes, databases, etc.), semi-structured (e.g., NoSQL databases), and unstructured (texts, presentations, etc.). Research claims that eighty percent of electronic health data is unstructured.
In general, data quality is affected by factors such as:
- inconsistency (data is inconsistent between sources within the same or different EHRs),
- incompleteness (there are gaps in the data),
- fragmentation (data is divided into pieces or blocks that may be stored out of order),
- inaccuracy (data is corrupted or contains errors),
- heterogeneity (unstructured and structured data are stored together).
How to address it?
Providers can improve the data collection by prioritizing high-value data types for their projects. High-value data include population-level information, genomic data, and electronic health records. Providers can also hire data management and integrity specialists. Clinical Documentation Improvement (CDI) also improves the data collection process. One CDI strategy, ambient intelligence, has been very effective. Nature reports that a provider spent 15 minutes instead of 2 hours to complete medical documentation when they used glasses with microphones attached to them. A deep learning model transcribed outpatient audio from conversations between patients and doctors with eighty percent accuracy. It outperformed the accuracy of medical scribes (seventy-six percent).
Data Cleaning
«Dirty» data can stall the workflow of a big data analytics project. For example, multiple disparate data sources capture clinical or operational elements in different formats. Analytics software cannot seamlessly combine them to provide an effective report.
Data cleaning, aka scrubbing or cleansing, is a process that is needed to ensure that data sets are correct, accurate, relevant, consistent, and not corrupted. It is best to cleanse the data immediately after the data is collected or as close to that point as possible. It is a recommendation from the Office of the National Coordinator for Health IT.
How to Address It?
Python web applications are common in the healthcare industry. There are many reasons for Python’s popularity. Programmers often claim that it is the easiest language to get started with. It is loved for its highly readable code. Medicine app developers choose Python because it is flexible and has many libraries and frameworks.
A healthcare technology company can hire Python developers to complement their engineering team. A Belitsoft expert Dmitry Baraishuk explains that developers collect and analyze past data that the customer company needs and carefully clean their data sets. They study the cleaned data and select the features that have the strongest relationship with the predicted variable. Next, the developers split the data set into test and training data. Only then do they train the Python model to detect patterns and make predictions. That is why data analysis with Python minimizes «dirty», heterogeneous data usage.
Data Storage
Healthcare data is growing exponentially. Healthcare organizations may prefer on-premise storage more than other industries because they are able to retain control of the data kept in-house. It places a heavy burden on local data centers to store the data. Additionally, scaling a local server network can cost a pretty penny for a healthcare organization. It is also difficult to maintain. If the data is divided into several parts, it can create silos of data across different departments.
How to Address It?
Cloud storage attracts payers and providers more and more. The price of cloud storage is no longer «biting» as it used to be, and the reliability of such storage is constantly growing. Another advantage of the cloud is that in the event of a failure, the company can quickly restore info from the cloud. Many organizations end up choosing a hybrid approach and use methods that are specific for each unique infrastructure. For example, clinical images need to be accessed quickly and often. It is easier to store it on a local server.
When a company chooses a cloud partner, as in any business, it needs to be careful, warns The HIPAA Journal. The cloud storage provider must be ready to sign a business associate agreement (BAA). This agreement ensures that partners are obligated to safeguard appropriately protected health information (PHI).
Data Security
Healthcare data is vulnerable to AI-powered cyberattacks, zero-day attacks (a vulnerability that hackers discovered before developers did), and other hackings and breaches. HIPAA requires healthcare organizations that store PHI to comply with security measures. But even the most secure data center is vulnerable if the organization’s employees are poorly versed in modern cybersecurity measures.
How to Address It?
Healthcare organizations must carefully follow cyber risk prevention strategies. First, it is necessary to educate all employees of the organization about how and why data security protocols are important. Second, the company’s employees must undergo regular cybersecurity training. Third, the organization must develop a local cybersecurity policy based on the tools, programs, and resources prepared by the U.S. Department of Health and Human Services.
Intermediate Bottom Line
This text is the first part of an analytical article dedicated to data analytics challenges in healthcare. The data collection, cleaning, storage, and security are the basis that most analytical internet resources write about. But there are other tasks for data analytics software, the solution of which will also help optimize processes and make the company’s analytics system truly effective. Read about them in the upcoming material «Healthcare Data Analytics: Challenges and Solutions. Part 2».
Also Read: How to Create a Healthcare Chatbot
