Technological developments in healthcare have changed the approach to looking at Patient related outcome data. Clinical Data Management (CDM) has become an essential process in clinical research that involves collecting, managing, and analyzing data from clinical trials.
Effective CDM can improve clinical outcomes data by ensuring that the data collected is accurate, complete, and reliable. They no longer exist just to manage operations or a business effortlessly. They have also become useful to unlock a plethora of real-time health information at every step of the value chain.
The Clinical Data Management process focuses on patient satisfaction and improving performance, care, resource usage, and business. Also, improve clinical outcomes data of all types of diseases, and bursts, and studies them closely.
What is the status of Clinical Data Management today?
Source: Diagnostic analysis based on a study by HBRAS Digital Diagnostic Transformation Webinar, April 2019
According to the Harvard Business Review Analytic Services (HBRAS)’s Global survey about data-driven diagnostics in healthcare in 2019, where most organizations believed Clinical Data Management across various settings is important but very few do it well. The Roche-sponsored survey, which covered senior healthcare executives, found some wide gaps. These gaps were between leaders and laggards in the adoption of data-driven practices and making decisions based on that data.
“A lot of our digital architecture [in health care] was not designed to be interoperable.”
—William Morice, Mayo Clinic
Leaders: Hospitals and healthcare institutions visualize healthcare data to learn the increasing trends for various diseases to make decisions on vaccine distribution, and medicines, and also to improvise on medical aid. Thus, data helps drive decisions, predictions, and outcome management in healthcare across different types of care settings.
Laggards: Diagnostic industry is yet to observe a complete shift in digital adoption. Data and technology manage business operations smoothly. Business analytics is utilized more often than clinical analytics (like test analytics) which proves in improving patient care through technology and promote patient satisfaction.
“The problem is we haven’t historically trained the majority of people working in health care to have a comfort around the use of data and an ability and a desire to use it.”
—Nick de Pennington, Oxford University Hospitals
However, there is a shift. Top diagnostic leaders now assess current methods, speed up and improve patient outcomes by treatment, and track inventory more ably.
Highlights of the survey findings –
- 95% OF ALL RESPONDENTS SAY CLINICAL DATA MANAGEMENT PROCESS ACROSS CARE SETTINGS IS VERY IMPORTANT
- 15% OF RESPONDENTS DESCRIBE THEIR ORGANIZATION TODAY AS BEING MATURE IN ITS ABILITY TO ACCESS, INTEGRATE, AND ANALYZE HEALTHCARE DATA FROM DIVERSE SOURCES
- 19% ARE VERY SUCCESSFUL AT MANAGING CLINICAL DATA ACROSS CARE SETTINGS TODAY
Diagnostics have been the laggard from the perspective of private and public investment in this space. Similarly, COVID-19 has served as a reality check for investment in tech in diagnostics. Now that the pandemic has changed the norms of managing diagnostic operations using technology, the industry too has begun to discover the importance of healthcare data analytics.
Improving patient care through technology has helped in many ways to learn the changing trends of COVID-19 across specific areas repeatedly and hence, the diagnostic analytics industry has begun to see it as a crucial tool. Know how you can aim for better clinical outcomes using the same.
The Future Potential of Diagnostic Analytics
Diagnostic Analytics is a means for prediction to help you learn different aspects of the conducted tests. Right from knowing the changes in the count (for an illness) in real-time or over a period to learning demographic relations of diseases w.r.t. city, gender, age, etc., analytics is useful to monitor the changing healthcare status and prevent it before it’s too late.
The COVID-19 seroprevalence survey was carried out using high-end clinical data management software. Use of findings from the survey to improve the course of treatment, setting up COVID-19 (quarantine centers), and improve testing quality. As a result, the pandemic is a testimonial of how we can leverage data-driven technological Clinical Data Management Software for improving healthcare data analytics in the future.
How Does the Clinical Data Management Process Improve Clinical Outcomes data?
- Predict outbreaks or increase of a specific disease – Monitor real time health data across geographies, and age groups. Then predict the outbreak of various seasons, common, lifestyle, and chronic diseases (based on historic data)
- Circumvent easy to prevent illness to improve patient outcomes
- Improves time and quality of care to patients – Looking at health trends using patients’ health records (For example, trends report)
- Improve Operational efficiencies – Better outcome management in healthcare including medical supplies and healthcare personnel, for example, if an outbreak is predicted in some area, resources can be distributed & diverted on a larger scale there to improve patient outcomes and avoid the poor clinical outcome
- Manage & redesigned quantitative approach – Have more setups or Diagnostic Analysis systems for frequently performed or increased number of tests, medical procedures, or care
- Reduce costs during season outbreak – Patients require care in the monsoon season when common cold, malaria, and dengue cases advance. Cost can be reduced at this time since numbers are higher than usual.
- Reach out to target patients by looking at recent data to market common health packages (general/senior citizen /full body checkups). This also includes special packages (for lifestyle diseases, chronic diseases, expecting mothers, and pediatric checkups)
Conclusion
Clinical Data Management is crucial for clinical research due to ethical considerations, data security, and regulatory compliance. Therefore, an effective CDM system in place ensures patient related outcomes and the success of healthcare data analytics.
