MORE THAN 2 MILLION YOUTHS EMPLOYMENT CHANCES ABROAD IN DATA COMPUTING /DIGITAL HAELTH FOR ELECTRONIC HEALTH RECORDS (EHR)

1????? .Worthy audience An Electronic Health Record (EHR) is an electronic version of a patient’s medical history that is maintained by the provider over time, and may include all of the key administrative clinical data relevant to that persons care under a particular provider, including?demographics, progress notes, problems, medications. As of 2023,?in USA/UK nearly 4 in 5 office-based physicians (78%) and nearly all non-federal acute care hospitals (96%) adopted a certified EHR. This marks substantial 10-year progress since 2011 when 28% of hospitals and 34% of physicians had adopted an EHR ..Folks EHR data analytics can?provide healthcare providers with actionable insights into patient behavior, trends, and patterns.Analyzing EHR patient data can provide real-time clinical decision support. The, adoption of advanced technologies such as Artificial Intelligence (AI) and Data Science has brought significant changes to the healthcare industry. As per the statistics, the physicians tend to devote about 62 percent of their valuable time per patient reviewing Electronic Health Records (EHRs), with the most amount of time being spent on clinical data review. With a huge amount of data being stored in EHRs, users could experience a type of information overload. In order to improve efficiency, artificial intelligence systems are being used that assist physicians in reviewing patient information. By incorporating artificial intelligence technology to work with these EHRs, physicians can avoid exhaustion and improve the patient experience.

• AI in EHRs is mainly applied for the improvement of data discovery, extraction, and personalized recommendations for treatments. Collaborating with a?business process outsourcing company ?is the best way to implement innovative AI technologies that can take healthcare businesses to the next level.
• ?Several advancements in medical imaging and the proliferation of clinical diagnostics and screenings generate large volumes of data on patient health. However, the main challenge with EHRs for large, integrated healthcare delivery systems is that they may prove to be inflexible, difficult to use and costly to configure. In addition, data regarding patient care procedures, patients, administrative processes, etc. also cannot be captured efficiently by EHRs.?
• There are limited options to bridge this gap between systems and procedures. Designing a streamlined EHR system can help in this regard, as they better fit into the workflows. The development of such integrated systems is often a time-consuming task. In such scenarios, open-source EHRs provide some break as the software is available free.?
• Even though the software is free, providing customized EHR systems will require a good amount of programming and infrastructure. In addition, these open-source systems are incautiously maintained as the systems are majorly designed for small medical practices.?

2???? . Worthy audience, the unique features of machine learning and natural language processing (NLP) can help record the unique medical experiences of patients, search the large EHR data banks for important documents, gauge patient satisfaction, etc. The machine learning models combined with NLP can help healthcare providers in transcribing the speech from the voice recognition system into text. The algorithms can be trained to deal with huge amounts of patient data related to the treatment, equipment used for treatment, respective doctor consulted etc. Further, this data can be segmented based upon the individual patient, illness, treatment for illness, etc. This will enhance the document and information search from the large databases. Here are some key applications of artificial intelligence (AI) in EHR systems:-

• Extraction of Data– Healthcare providers can extract patient data from various sources such as clinical data, provider notes, fax etc. by leveraging AI and recognizing key terms that reveal actionable insights.
• Clinical Documentation– It is estimated that physicians spend approximately one-third of their time creating notes and reviewing medical records in the electronic health record (EHR). This may be related to bolstering ongoing care to help patients achieve positive health outcomes (for example, ensuring continuity of care for the patient between venues). In fact, this comes at a significant cost. As payment models become more complex, physicians are seeking ways to improve clinical documentation. The role of AI arises at this point. This is particularly true for the patient-physician encounter during the clinical validation or data reviews conducted for reimbursement, research and quality improvement. Healthcare companies leverage AI to develop NLP-powered tools that can be integrated with the EHRs to capture data from the clinical notes, thereby allowing physicians to focus more on their patients and the treatments.
• Predictive Analytics– Predictive models from Big Data will help alert the physicians of potentially deadly diseases. In addition, AI technology can power up medical image interpretation algorithms that could be further integrated into the EHRs and provide decision support and treatment strategies.
• Clinical Decision Support– Decisions regarding further treatment procedures and options are usually generic in nature. With AI technology incorporated into the systems, more machine learning solutions that enable personalized care are emerging. AI-based clinical decision support systems have the ability to analyze a huge volume of data and suggest further steps for treatments, indicate potential problems, enhance efficiency, and facilitate the work of healthcare providers. It can also leverage the information collected from patients’ health data. Incorporating AI- based technology in EHR is important for two reasons. Firstly, the huge amount of clinical data available can be obtained in real-time from medical devices and records. Secondly, this vast amount of data can be quickly and efficiently analyzed and optimally used.

3??? .EHRs act as a life raft during an emergency by offering the complete medical history of the patient. As the information is stored electronically, it can be used by healthcare providers to access patient data from any location. When artificial intelligence is integrated into the healthcare system, physicians can use it as a tool to provide better and more efficient patient care. Not only does it improve productivity, but can also lighten the load for physicians. This helps improve communication between physicians and patients, thereby improving patient care. By implementing AI into EHR systems, healthcare institutions can significantly improve operational efficiency and productivity. Experienced providers of?business process outsourcing solutions ?can ensure the accuracy of healthcare processes and patient data.The EMRs bring greater benefits summarized as follows:

• EMR helps in reducing and minimizing medication errors, which benefits the patients and the doctors
• It reduces the transcription errors which are common in handwritten medical records
• It eliminates the concept of missing of the medical files
• Better and faster decision-making and improved clinical care process
• Digital record environment saves space, which is always a huge constraint in hospitals
• Better diagnosis and aided by drug delivery system for patient management and better quality care in terms of treatment
• It minimizes the operational cost by eliminating unnecessary overtime labor costs.

4?? ??. This Article concludes that EMR is the future of patient-centric medical care, and its adoption is going to increase only in the coming times. However, the physicians should be involved from the procurement stage of EMR, and to increase the usage of EMR by physicians, the hospital administration should focus on four factors i.e., to improve the positive attitude toward EMR, reduce the difficulty level of using the EMR, select a proven and reliable EMR, procure an EMR which can be tailored to the needs and specifications of physicians, i.e., an adaptable EMR. Economic, health and geopolitical trends have created divergent outcomes for labour markets globally in 2023.?While tight labour markets are prevalent in high-income countries, low- and lower-middle-income countries continue to see higher unemployment than before the COVID-19 pandemic. On an individual level, labour-market outcomes are also diverging, as workers with only basic education and women face lower employment levels. At the same time, real wages are declining because of an ongoing cost-of-living crisis, and changing worker expectations and concerns about the quality of work are becoming more prominent issues globally. The Future of Jobs Survey brings together the perspective of 803 companies – collectively employing more than 11.3 million workers – across 27 industry clusters and 45 economies from all world regions. The Survey covers questions of macro trends and technology trends, their impact on jobs, their impact on skills, and the workforce transformation strategies businesses plan to use, across the 2023-2027 timeframe.

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