help me pleaseRequirements Engineering for Health Data Analytics Human body is probably the most complex system the nature has crated ever since, and knowledge to many parts of human body remains to be unknown. Thus, it is inevitable that complexity is the most important feature of issues related to healthcare, due to the uncertainty and the overwhelming choices faced by practitioners when making decisions Daily clinical procedures involve the examination of patients’ health conditions in shu to make diagnosis, and the real-time monitoring of the vital signs for patients in acute condition can help save lives The current Big data wave has brought disruptive changes to the healthcare industry with the hope of innovative healthcare and medical information management approaches to extract interesting insight from the massive clinical data to answer important questions related to population health, and enable the creation of new values from health information at used to be difficult or even impossible Big data requires real-me analysis of large datasets. calis for distributed system architectures for data acquisition storage and processing mechanisms Generic data services modules data generation, acquisition, storage, and analytics form a big data value chain Software system requirements are driven by business and clinical requirements Requirements engineering usually adopts a problem-driven process Firsta problem objective is identified, then the satisfying criteria for alternative system designs is defined. On the other hand, data analytics applications are often taking a solution-oriented approach, ie, there is a given dataset and some common techniques and tools for which feasible problems are identified and experimented with. Thus, requirements engineering for data analytics is expected to bridge the two mindsets, matching the right problem with the right accessible data and techniques The RE process for data analytics has to answer the following questions If data analytics technology is the solution, what should the relevant problems be? What can be done with the accumulated data? Collection, storage, retrieval, and processing of data impose costs, so upon what standards can decisions be made related to big data and their analytics? For what purpose and how should the data be collected? What kind of data management infrastructure is good enough to support the usage of the data? What analytical functions are required to serve the intended aims? Shall we treat these issues case-by-case, or treat them using a common standard procedure? DROP-based requirements elicitation is suggested for the medical domain, where D means Domain expertise acquisition, R – Raw data collection first, the sharing of Ownership, and P. Prototype-driven perfection of products D, R. O, and P can be applied sequentially In domain expertise acquisition depending on the particular function under design, the right stakeholder should be identified. In raw data collection, in order to collect useful data, we must understand the data sources currently available. The starting point could be paperbased forms, or current systems and databases Collecting raw data can help us understand the basic data structure and obtain an intuitive perception of the data scale and data format, and figure out redundancy and deficiencies for a given functional goal. In sharing of ownership, the mutual agreement on an ownership sharing strategy, the bond of the two parties: doctors and developers have to be established as both parties are contributing to the requirements and solutions Health IT projects require fast deployment, i.e., there has to be a working product in place to keep the project rolling Rapid prototype is still the best practice in the field. After a few iterations, the requirements become concretized in the prototype. Then the development team can go back and optimize the design for better overall quality and performance in future envisaged use. Show transcribed image text Requirements Engineering for Health Data Analytics Human body is probably the most complex system the nature has crated ever since, and knowledge to many parts of human body remains to be unknown. Thus, it is inevitable that complexity is the most important feature of issues related to healthcare, due to the uncertainty and the overwhelming choices faced by practitioners when making decisions Daily clinical procedures involve the examination of patients’ health conditions in shu to make diagnosis, and the real-time monitoring of the vital signs for patients in acute condition can help save lives The current Big data wave has brought disruptive changes to the healthcare industry with the hope of innovative healthcare and medical information management approaches to extract interesting insight from the massive clinical data to answer important questions related to population health, and enable the creation of new values from health information at used to be difficult or even impossible Big data requires real-me analysis of large datasets. calis for distributed system architectures for data acquisition storage and processing mechanisms Generic data services modules data generation, acquisition, storage, and analytics form a big data value chain Software system requirements are driven by business and clinical requirements Requirements engineering usually adopts a problem-driven process Firsta problem objective is identified, then the satisfying criteria for alternative system designs is defined. On the other hand, data analytics applications are often taking a solution-oriented approach, ie, there is a given dataset and some common techniques and tools for which feasible problems are identified and experimented with. Thus, requirements engineering for data analytics is expected to bridge the two mindsets, matching the right problem with the right accessible data and techniques The RE process for data analytics has to answer the following questions If data analytics technology is the solution, what should the relevant problems be? What can be done with the accumulated data? Collection, storage, retrieval, and processing of data impose costs, so upon what standards can decisions be made related to big data and their analytics? For what purpose and how should the data be collected? What kind of data management infrastructure is good enough to support the usage of the data? What analytical functions are required to serve the intended aims? Shall we treat these issues case-by-case, or treat them using a common standard procedure? DROP-based requirements elicitation is suggested for the medical domain, where D means Domain expertise acquisition, R – Raw data collection first, the sharing of Ownership, and P. Prototype-driven perfection of products D, R. O, and P can be applied sequentially In domain expertise acquisition depending on the particular function under design, the right stakeholder should be identified. In raw data collection, in order to collect useful data, we must understand the data sources currently available. The starting point could be paperbased forms, or current systems and databases Collecting raw data can help us understand the basic data structure and obtain an intuitive perception of the data scale and data format, and figure out redundancy and deficiencies for a given functional goal. In sharing of ownership, the mutual agreement on an ownership sharing strategy, the bond of the two parties: doctors and developers have to be established as both parties are contributing to the requirements and solutions Health IT projects require fast deployment, i.e., there has to be a working product in place to keep the project rolling Rapid prototype is still the best practice in the field. After a few iterations, the requirements become concretized in the prototype. Then the development team can go back and optimize the design for better overall quality and performance in future envisaged use.
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