Healthcare Consolidation: How Data Transformation is Important?
It is astounding to consider that the volume of data collected in the previous few years alone has surpassed that of all of human history. As a result, current technology has struggled to deal with the enormous amount of information, resulting in subpar performance, lost profits, and wasted time. According to Forbes, 92% of firms struggle with data and data management.
As a dynamic industry, healthcare is constantly implementing new technologies, allowing it to collect more accurate and complete patient data. With the advancement of technology, patient data can be available system-wide immediately, and collaborative efforts between medical systems can improve medical data accuracy.
It is essential to have good quality patient data to inform clinical care, and everyone should have access to their own healthcare information to help manage their health conditions. Patient’s medical history and the most recent patient data collected about them need to be accessible to the whole care team in order to guide treatment and provide the best possible care.
Data is Key in Healthcare
Maintaining current patient information is crucial in modern healthcare. Doctors can gather necessary patient data and patient charts simultaneously at the point of service or immediately afterward by accessing the tools and information systems that host the data. However, synthesizing data across systems and tools can still be a challenge. With modern data technologies, quick and accurate data can be made available quickly to the care team. This is true for individual patient information as well as the wider group of patient populations. In today’s modern healthcare, reliable data can be the difference between life and death and clinicians require immediate access to the most current and accurate patient data available at every stage of the treatment process.
Healthcare data analytics solutions can enhance overall healthcare services by predicting illness, outbreaks, lowering treatment costs, and preventing diseases.
The importance of data transformation in healthcare includes:
- Well-informed decisions - In healthcare ‘Patient care’ holds the key, followed by cost, and finally profit. Complex patient data collection and storage in one location can lessen the frequency of follow-up doctor visits and assist in preventing avoidable and expensive readmissions. Medical data-collecting technologies make it feasible to gather and analyze patient data and outcomes rapidly and efficiently. Quick and secure information interchange between doctors, caregivers, insurance companies, and other parties is necessary in order to improve treatment and perhaps save lives.
- Market competitiveness - The quality of services provided to patients might be greatly enhanced by the proper harnessing of patient data, which would also result in improved market positions and higher profits. Research suggests healthcare industries can save 25% of healthcare costs by improving data collection and analysis annually. Healthcare providers may employ the most recent data gathering, analysis, and software techniques to achieve the desired outcomes.
- Reduction of costs - Accurate data is especially important in healthcare when it involves individual care. In hospitals and other medical facilities, thorough and in-depth diagnosis and treatment may result in both better outcomes and reduced expenses. Based on McKinsey's study, efficient data collection in healthcare and analysis could save the U.S. medical system $300 billion per year.
Healthcare Data Challenges
Any analytics-based solution's effectiveness depends on the quality, security, and interoperability of the available data. In this regard, certain pain points in data storage and handling in the healthcare industry include:
- Data Security - Sensitive data security and protection have long been a crucial concern, for the healthcare industry. When it comes to data storage, healthcare providers need to contend with a plethora of rules and regulations. With the recent rise in cyber threats, it is even more important to be able to protect confidential patient information. The use of encryptions, cloud-based infrastructure, and real-time infrastructure threat monitoring are some ways to proactively manage data security challenges.
- Data Quality - Data quality emerges as one of the biggest challenges in the healthcare industry. Any instance of data duplication might wind up costing up to $1,950 per patient for each inpatient stay and more than $800 for each ER visit. The process of medical data collection itself contributes to the quality of data. Handwritten, scanned data are prone to human errors resulting in low-quality patient data. Focusing on improving data quality on an ongoing basis is key to improving data accuracy. With latest improvements in machine learning techniques, data quality can be managed better and faster.
- Difficulties in integration and collaboration - When data analytics techniques are used in healthcare settings, various types, and formats of data are merged like genomics and post-genomics, sensor informatics, Electronic Health Records (EHRs), bioinformatics, sensor informatics, medical informatics, medical imaging, and health informatics. However, inconsistent formats, proprietary definitions and multiple standards can make integration and collaboration of this data prone to inaccuracies resulting in a challenge for the healthcare industry.
Data Transformation Stakeholders in Healthcare Settings
Researchers, doctors, hospital managers, and executives from the insurance business are some of the persons in charge of carrying out data analytics in diverse healthcare settings. However, they may also work directly with modern tools that include graphical user interfaces for those without analytics expertise. Healthcare practitioners collaborate with persons skilled in the application of complex data analytics approaches.
The most typical healthcare stakeholders that employ data analytics include:
- Clinical practitioners - By better planning and staffing, giving patients more alternatives for arranging appointments and getting care, and predicting which patients are most at risk for readmission, data analytics in clinical settings aim to decrease patient wait times.
- Healthcare payers - Insurance companies employ data analytics to verify that they adhere to constantly changing legislation, target common diseases by analyzing claims and prescriptions, and correlate price information with quality indicators to find high-quality, low-cost healthcare providers. Predictive analytics is another tool used by insurance companies to identify potentially fraudulent claims and alert providers of such claims.
- Population health managers - Professionals in public health are placing more emphasis on prediction and prevention than on reaction and treatment. In the early stages of a disease, predictive analytics can be used to identify individuals who are most at risk for developing a chronic condition. Reduced risk of chronic disease decreases total healthcare costs and improves patient outcomes. This risk is reduced by analysis of lab tests, claims data, patient-generated data, and numerous societal variables.
How Healthcare Industry Can Leverage Data Transformation?
With the criticality of data in both the patients care journey and the performance of the providers and payers, stakeholders can implement data transformation initiatives in focused areas like:
- Using patient data to improve health outcomes - In healthcare settings, quality improvement aims to provide patients with safe, efficient care while decreasing the stress connected to such care. To accomplish this, healthcare professionals gather and analyze patient data, increasingly in real-time, to better understand the complex healthcare environments of today, to create and implement a systematic approach to improve patient outcomes, and to continuously create, test, and implement improvements to healthcare processes.
- Analyzing clinical data to improve medical research - By collecting and analyzing clinical data from diverse sources, data analytics techniques are employed to enhance research efforts in numerous health-related fields. The findings aid in the development of strategies for increasing the effectiveness of clinical procedures and other healthcare activities. Through individualized healthcare delivery, the research results in more accurate diagnosis and treatment. By establishing a connection between risk factors and health outcomes, cohort studies give medical researchers a fresh understanding of the origins of disease.
- Gaining operational insights from healthcare provider data and improving staffing through health business analytics - Healthcare providers' internal working methods and organizational setup significantly affect the standard of treatment patients get and the likelihood of successful patient outcomes. It is necessary to be able to gather and analyze healthcare provider data linked to these processes and structures in complex healthcare environments in order to evaluate the impact of process and organizational changes on outcomes and operational efficiency. An organization-wide evaluation of the existing practices and the alignment of the institution's improvement plans and goals are the first steps toward raising the quality and effectiveness of healthcare. Employee scheduling, charting, emergency planning, administration, financial management, and compliance are the operational sectors most likely to gain from data analytics. Along with this, health business management can use data analytics to pinpoint staffing challenges to find, hire, develop, and retain medical personnel. About 60% of hospital budgets are devoted to labor expenditures, and future salary and compensation increases are anticipated due to the growing need for healthcare workers. Healthcare providers must strike a compromise between the expanding medical workforce scarcity and the need to control costs, increase efficiency, and enhance patient outcomes.
Increasing Importance of Data in Healthcare
The use of data analytics in healthcare has already had a significant influence on healthcare professionals' capacity to provide patients with high-quality treatment in an effective, efficient manner. Understanding how the technology can be applied to address challenges faced by healthcare providers, such as staff recruitment and utilization, operational efficiencies, and improved patient experiences, is the first step in realizing the potential of data analytics to transform the healthcare industry. Knowing what patients want and need is essential for providing patient-centered care. The key to gaining access to this crucial knowledge is data analytics and transformation.
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