solved please reply to the following DQs with 150-200 words each.

please reply to the following DQs with 150-200 words each. Thank youMiriam:The collection and evaluation of healthcare data from multiple sources are critical for optimizing patient care. Healthcare data analytics increases patient-provider communication and gives clinicians a more in-depth understanding of specific health concerns(Coquerel, 2021).In a nutshell, data normalization is the process of taking a source terminology and matching it to the most appropriate term in the destination technology. Medical terminologies coming from disparate systems, whether inside or outside an organization, must be normalized to standard terminologies before they can become actionable knowledge. Raw data in the form of incoming and internal medical terminology must be normalized and mapped to standardized code sets and terminologies such as ICD-9/10, SNOMED CT, LOINC (for labs), and RxNorm (for drugs). Once this piece of the interoperability equation is reconciled, the industry can achieve a more accurate picture of performance for better reporting and analytics. There are a lot of ways to say the same thing in medicine – whether that’s by using a colloquial term (heart attack), a more clinical one (myocardial infarction), or a billing code (121.9, acute myocardial infarction, unspecified). However, when looking to glean insights from healthcare data, it’s important to make sure that all of these different variations are harmonized under one clinical term so that everyone is speaking the same language.ReferencesCoquerel, J.-L. (2020). Different Types of Healthcare Data. Www.syntrixconsulting.com. https://www.syntrixconsulting.com/blog/different-t…Vanessa;Having knowledge about different types of data enables you to choose an accurate technique of analysis. Having an understanding of different data types is a key condition for performing Exploratory Data Analysis (EDA) because you can use only a specific statistical measurement for certain data types.As stated by Perspective.ahima.org, “Clinical coding constitutes one of the fundamental functions in the field of health information management. Clinical classification systems and clinical terminologies represent two distinct sets of coding schemes that are used in healthcare. In this context, it is critical to distinguish between clinical terminologies and clinical classification systems, identify how both sets of systems are utilized in healthcare settings, and acknowledge individual contributions of each system to providing data infrastructure for clinical as well as administrative data uses in the healthcare delivery system” (Alakrawi, 2016)A reference phrasing can be characterized as a bunch of ideas and connections that give a typical reference highlight comparisons and aggregation of data about the whole medical services process, recorded by numerous various people, frameworks, or foundations. Arranged Nomenclature of Medicine–Clinical Terms (SNOMED CT) addresses an illustration of clinical phrasings utilized in medical services. SNOMED CT is a normalized medical services phrasing that was initially evolved from a pathology-specific nomenclature called Systematized Nomenclature of Pathology. SNOMED CT is a controlled clinical wording that incorporates infections, clinical discoveries, etiologies, techniques, and wellbeing results. It tends to be utilized by doctors, medical caretakers, partnered wellbeing experts, veterinarians, and scientists.Reference:Zahraa M. Alakrawi, MS. “Clinical Terminology and Clinical Classification Systems: A Critique Using AHIMA’s Data Quality Management Model.” Perspectives in Health Information Management (Summer 2016): 1-19.Kari:As Raghupathi and Raghupathi indicated in their article Big data analytics in healthcare: promise and potential, “Big data encompasses such characteristics as variety, velocity and, with respect specifically to healthcare, veracity.” The four V’s are an example of the purpose of looking at different types of data.Volume: “health-related data will be created and accumulated continuously, resulting in an incredible volume of data. The already daunting volume of existing healthcare data includes personal medical records, radiology images, clinical trial data FDA submissions, human genetics, and population data genomic sequences, etc. Newer forms of big data, such as 3D imaging, genomics, and biometric sensor readings, are also fueling this exponential growth. (Raghupathi & Raghupathi, 2014).”Variety: “The enormous variety of data—structured, unstructured, and semi-structured—is a dimension that makes healthcare data both interesting and challenging. … But increased variety and high velocity hinder the ability to cleanse data before analyzing it and making decisions, magnifying the issue of data “trust”. (Raghupathi & Raghupathi, 2014).”Velocity: “Data is accumulated in real-time and at a rapid pace, or velocity. The constant flow of new data accumulating at unprecedented rates presents new challenges. Just as the volume and variety of data that is collected and stored has changed, so too has the velocity at which it is generated and that is necessary for retrieving, analyzing, comparing, and making decisions based on the output. … Velocity of mounting data increases with data that represents regular monitoring, such as multiple daily diabetic glucose measurements … [and] can mean the difference between life and death. … And as indicated prior, increased variety and high velocity hinder the ability to cleanse data before analyzing it and making decisions, magnifying the issue of data “trust” (Raghupathi & Raghupathi, 2014).”Veracity: “Some practitioners and researchers have introduced a fourth characteristic, veracity, or ‘data assurance’. That is, the big data, analytics, and outcomes are error-free and credible. Of course, veracity is the goal, not (yet) the reality. …Veracity assumes the simultaneous scaling up in granularity and performance of the architectures and platforms, algorithms, methodologies, and tools to match the demands of big data (Raghupathi & Raghupathi, 2014).”What is the role of clinical terminology in data aggregation, normalization, and reconciliation?A report delivered to the U.S. Congress in August 2012 defines big data as “large volumes of high velocity, complex, and variable data that require advanced techniques and technologies to enable the capture, storage, distribution, management and analysis of the information. … Ideally, individual and population data would inform each physician and her patient during the decision-making process and help determine the most appropriate treatment option for that particular patient (Raghupathi & Raghupathi, 2014).”Also, [p]otential benefits include detecting diseases at earlier stages when they can be treated more easily and effectively; managing specific individual and population health, and detecting health care fraud more quickly and efficiently. Numerous questions can be addressed with big data analytics. Certain developments or outcomes may be predicted and/or estimated based on vast amounts of historical data, such as length of stay (LOS); patients who will choose elective surgery; patients who likely will not benefit from surgery; complications; patients at risk for medical complications; patients at risk for sepsis, MRSA, C. difficile, or other hospital-acquired illness; illness/disease progression; patients at risk for advancement in disease states; causal factors of illness/disease progression; and possible co-morbid conditions (Raghupathi & Raghupathi, 2014).”The authors’ state that in their research IBM suggests big data analytics in healthcare can contribute to:Evidence-based medicine: Combine and analyze a variety of structured and unstructured data-EMRs, financial and operational data, clinical data, and genomic data to match treatments with outcomes, predict patients at risk for disease or readmission and provide more efficient care; Genomic analytics: Execute gene sequencing more efficiently and cost-effectively and make genomic analysis a part of the regular medical care decision process and the growing patient medical record;Pre-adjudication fraud analysis: Rapidly analyze large numbers of claim requests to reduce fraud, waste, and abuse;Device/remote monitoring: Capture and analyze in real-time large volumes of fast-moving data from in-hospital and in-home devices, for safety monitoring and adverse event prediction;Patient profile analytics: Apply advanced analytics to patient profiles (e.g., segmentation and predictive modeling) to identify individuals who would benefit from proactive care or lifestyle changes, for example, those patients at risk of developing a specific disease (e.g., diabetes) who would benefit from preventive care.ReferencesRaghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: Promise and potential. Health Information Science and Systems, 2(1), 3. https://10.1186/2047-2501-2-3