Healthcare data is expected to constitute ~36% of the world’s data volume by 2025 (see Figure 1).1 Data management in healthcare can be challenging because:
- ~80%of healthcare data remains unstructured after its creation.
Content services healthcare applications can assist healthcare organizations in data management. In this paper, we inform healthcare executives about healthcare content services (CS) applications.
Figure 1: Data growth in healthcare, other sectors, and global data pool.
Content Services Healthcare Applications
HIPAA violations imposed about $5.9M penalty on healthcare companies in 2021. ~$800K was due to noncompliance to HIPAA Right of Access, which forces healthcare companies to provide patient records within thirty days of request. Nearly $5M was imposed on a single company because of patient data breaches by hackers using malware.2
Content services (CS) platforms can improve patient data management data security and compliance with HIPAA regulations with:
- Digital Signatures: CS platforms provide digital signatures that can encrypt patient files so they cannot be changed, and it adds a time stamp to the creation time of the signature. This helps companies comply with regulation demands that Electronic Patient Health Information (e-PHI) should not be changed or destroyed.
Figure 2: Sorting medical documents is time-consuming.3
- Audit-proof archive: HIPAA Retention Requirements demand that patient documents be retained unaltered for a minimum of six years upon their creation or from the last time it was in effect.4 CS platforms can lock patient documents to remain audit-proof until the HIPAA requirement expires. When these locks have been applied to a patient document:
Over the last decade, the digital world has expanded rapidly beyond 16 zettabytes, giving rise to internet giants storing over 30 petabytes of data.5 This gave rise to the term ‘Big Data,’ which the IT industry has used to generate significant revenue.
In healthcare, big data consists of
- Pharmacy prescriptions
- Insurance records
- Genomics-driven experiment data, such as gene expression data
- Smart web of IoT
Figure 3: Big data in healthcare.6.
CS platforms can be applied to:
- Gene expression data: CS platforms can enrich, ingest, and store genetic sequence engine results.
- Workflow management: CS platforms can offer workflow management systems to standardize and automate analysis and interpretation processes. For example, workflow management can automate genomics data processing to reduce manual effort.
A good portion of big data consists of unstructured information. For example, patient data in healthcare contains recorded signals like electrocardiograms (ECG), pictures, and videos. This data can be used in predictive analytics to diagnose and treat illnesses. However, to use this data, it needs to be easily accessible and machine-readable.
CS platforms can offer solutions to
- Data conversion: CS can convert data to machine-readable formats. For example, it can use OCR technology to convert hand-written pre-EHR records to digital formats. They can convert results from imaging such as MRI to machine-readable formats for diagnosis in ML models.
- Storage: Storing big data is a concern, and for predictive analytics, data size can be an important factor for accurate predictions. CS platforms can store large sizes of data on the cloud. For example, projects like the National Institutes of Health Cancer Genome Atlas store large sizes of genomics data, about 2.5 petabytes, to analyze genomic factors of cancer.
Doctors can spend more time in patient care if the document search time can be reduced. According to a study, doctors spent about 16% of their time on direct or patient care, 60% of their time looking at their notes, and 13% searching paperwork and medical equipment.7 Assuming that doctors look at their notes for indirect patient care, about 76% of their work time is spent on patient care and 13% on searching.
- Electronic medical records (EMR) management: CS platforms can offer an EMR system to reduce patient record search time and protect patient data. On CS platforms, doctors can:
- Record symptoms
- Track patient data
- Administration: CS platforms can also save time from daily administrative tasks in healthcare services. The CS platform can automate such tasks:
- Pharmacies and blood banks: Keeping a record of incoming and outgoing drug stock or blood in spreadsheets can be time-consuming and prone to error. CS platforms can help inventory management in pharmacies and blood banks.
For more information on CS platforms in healthcare, don’t hesitate to get in touch with us:
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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