NIT 6130 -Electronic Health Record System
In the report, complexities in electronic health record system are discussed. It is identified in the research study that electronic health record system play very important role in management of patient records. In the research, the data is collected related to electronic health record system for reducing the complexities. In the research, the samples are also collected from the electronic methods from different hospitals using the electronic health record system.
Further, research highlights the opinion and reviews from the medical professionals like doctors to manage the system for reducing the complexities in electronic health record system. The questionnaire of electronic health record system is also being designed on the basis of research study.
The data collection is very important step before starting any research, analysis process, experiment student and study process. The data is collected through various sources like journals, PDFs, articles, and conferences for analysing study complexities in electronic health record system.
The issues that are present in electronic health record system are reduced with the effective data collection from this research study (Mishuris & Linder, 2012). The use of electronic health record system is enhanced in hospitals with the complexity reduction. In the study, the feedback and opinion is received from the doctors and electronic professionals in electronic health record system context.
Before studying the complexities of electronic health record system the issues and limitations of the research are focussed so the sources are selected effectively. Before starting the research study of electronic health record system complexities the data is collected through the sources that give reliable and accurate data (Mishuris & Linder, 2012). The sources by which the data is collected are:
- Conference papers
- Review and opinion from electronic professionals like doctors
The collection of data is done through questionnaire and online resources in which records are stored for health record. The data explanation, data sources, data charges and target source helps in reducing the data complexities (Mishuris & Linder, 2012).
After the collection of data from different sources and by analyzing the electronic health record systems the data storage table is designed in which the complexity of data in maintaining electronic health record system is reduced and efficiency in data accessing is increased (Wu, et. al., 2013).
Deign and implement experiments
The data is effectively collected and stored in electronic health record system for managing complexity of patient’s data. In next stage of design and implementation, the data pre-processing, designing and implementing step take place. The electronic health record system faces complexity during access of data due to security issues during data pre-processing. The secured system feature is selected that helps database implementation of electronic health record system (Byrd, et. al., 2013).
The complexity of data is minimized by reducing the raw data or by conversion of unuseful data into featured data. It is the most efficient stage in which the missing values, outer range of values, inaccurate values, etc. are identify effectively and transform into some usable form. It is the essential process in which the data complexity is varied from system to system for managing health records (Middleton, et. al., 2013). The data cleaning, data filtering and duplicity of data can also be solved through data pre-processing step this results the complexity free system for maintaining health records. The pre-processing steps are:
The first step is reading of data where raw data is read by user who uses the electronic health record system. In this system, the data is filtered by separating raw data through classification of data such as structured and unstructured data. After filtering the data is arranged in a structured manner so the pr-processing is applied effectively. After pre-processing of data the effective data is evaluated from the raw data. The results are generated or specialize in new results (Wu, et. al., 2013). The system with new effective data in electronic health record system with reduced complexity is produce.
Feature selection or dimension reduction
After the pre-processing of electronic health record system data, the next stage is complexity reduction through useful feature selection from collected results (Weiskopf & Weng, 2013). The table that is shown below is reducing the data complexity that is done on the pre-processing step
Detailed Design Steps
In the research study of electronic health record system, the complexities are found that can be reduced through pre-processing. In the research study hybrid approach is used that support both quality and quantity with argument. The information of data is collected, analyzed and search from the registered journals, conferences and links for identification of complexity in electronic health record system (Wu, et. al., 2013). Further, it can say that the data which is collected is accurate and effective in evaluating the complexity of electronic health record system. The data comparison and evaluation is done through the responses and reviews of the doctors, staff, and hospitals are designed from questionnaire.
Before survey, some features are collected from different gender type, hospital type, doctor type, and people are identified so, the complexity in system is identified effectively. The identification of features and issues in electronic health record system get the rough idea that how the system they currently required and what are the preferences. With the study, the feature and dimension for electronic health record system is selected effectively so the data complexity is reduced in system (Miotto, et. al., 2016).
The above table shown the questionnaire and responses by which data is collected are presented by which survey is done. The information from which the complexity is identified for the electronic health record system is discussed so that the complexity is reduced in electronic health record system so the accuracy in using the system is achieved (Miotto, et. al., 2016).
Software and Tools used
In implementing the survey the tools and software are used in effective way. For effectively evaluating the process of implementation data is stored in the tabular form so later review will be done (Meeks, et. al., 2014). In results in table 6with features are shown for the participants.
Result analysis and summary
It is identified that the hospitals and medical professionals face complexity and difficulty with the implementation of the electronic health record system. It occurs due to large amount of patient records and the unusual information is stored in the system. The responses from doctor and medical professional gave the feature of electronic health record system. Through the research, it is seen that the electronic health record system is effective in data collection for the patients. The system is used in the hospitals for generating records and also identification of previous records for patient. The system reduces the complexity gap of doctor and patient that they face at time of treatment.
It is seen that the electronic health record system are using for other purpose as well as for generating daily routine sleep and health fitness pattern of patient so the doctors analyze the patient condition effectively. The electronic health record system is useful in private as well as for government hospitals (Charles, Gabriel & Furukawa, 2013). The patient records are managed effectively through the electronic health record system. So, in the study, it is identified that the large amount of hospital and doctor found that the system is useful for them. As time the small clinic are also going towards the electronic health record system for reducing their work and efforts (Charles, Gabriel & Furukawa, 2013).
In the research study, the analysis is done in a detailed manner for the electronic health record system complexity. Through various trusted sources the data is collected for the identification of complexity in system. The electronic health record system reduces the doctor and hospitals efforts in managing of patient health records (Mishuris & Linder, 2012). With various respondents like doctors, nurse and hospital’s staff the survey is completed. In the survey different categories of hospital and doctors are taken like allergist of private and government hospitals (Wu, et. al., 2013).
The results that are shown above helps in reducing the complexity of electronic health record system with new added features. The responses, opinion, and review from the respondents support in reducing the complexity of electronic health record system (Hsiao, Hing& Ashman, 2014). The new features with improved complexity are added as per preference of doctors and medical professionals. These new features contribute in improving the accuracy and effectiveness of health record system that satisfy the needs of doctors and patient (Bayley, et.al., 2013).
From the research study, it is concluded that electronic health record system plays major role in the medical industry. The data is collected from the doctors, hospitals, and clinics. Every hospitals and doctors are reviewed for the electronic health record system. The electronic health record system includes different data formats to manage records in hospital. The doctor provides the positive reviews after the reduction of complexities in electronic health record system. The survey is done through developing questionnaires and collecting the responses from persons which are recorded for the electronic health record system. The implementation of secured system in EHR helps in managing data security and privacy concern among people.
- Bayley, K. B., Belnap, T., Savitz, L., Masica, A. L., Shah, N., & Fleming, N. S. (2013). Challenges in using electronic health record data for CER: experience of 4 learning organizations and solutions applied. Medical care, 51, S80-S86.
- Byrd, J. B., Vigen, R., Plomondon, M. E., Rumsfeld, J. S., Box, T. L., Fihn, S. D., & Maddox, T. M. (2013). Data quality of an electronic health record tool to support VA cardiac catheterization laboratory quality improvement: the VA Clinical Assessment, Reporting, and Tracking System for Cath Labs (CART) program. American heart journal, 165(3), 434-440.
- Charles, D., Gabriel, M., & Furukawa, M. F. (2013). Adoption of electronic health record systems among US non-federal acute care hospitals: 2008-2012. ONC data brief, 9, 1-9.
- Hsiao, C. J., Hing, E., & Ashman, J. (2014). Trends in Electronic Health Record System Use Among Office-based Physicians, United States, 2007-2012. US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics.
- Meeks, D. W., Smith, M. W., Taylor, L., Sittig, D. F., Scott, J. M., & Singh, H. (2014). An analysis of electronic health record-related patient safety concerns. Journal of the American Medical Informatics Association, 21(6), 1053-1059.
- Middleton, B., Bloomrosen, M., Dente, M. A., Hashmat, B., Koppel, R., Overhage, J. M., … & Zhang, J. (2013). Enhancing patient safety and quality of care by improving the usability of electronic health record systems: recommendations from AMIA. Journal of the American Medical Informatics Association, 20(e1), e2-e8.
- Miotto, R., Li, L., Kidd, B. A., & Dudley, J. T. (2016). Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Scientific reports, 6, 26094.
- Mishuris, R., & Linder, J. (2012). Electronic Health Records and the Increasing Complexity of Medical Practice: “It Never Gets Easier, You Just Go Faster”. Journal Of General Internal Medicine, 28(4), 490-492.
- Weiskopf, N. G., & Weng, C. (2013). Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. Journal of the American Medical Informatics Association, 20(1), 144-151.
- Wu, A. W., Kharrazi, H., Boulware, L. E., & Snyder, C. F. (2013). Measure once, cut twice—adding patient-reported outcome measures to the electronic health record for comparative effectiveness research. Journal of clinical epidemiology, 66(8), S12-S20.
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