MPI is an electronic medical database that stores information about all patients registered with the human services association. In some cases, MPI may include information about medical personnel and other employees. The MPI stores biodata such as the patient's name, gender, date of birth, contact information, race, and place of residence, as well as a medical home and a standardized savings number. It is critical to ensure that all of the important data required by the social insurance association is reflected in a single database. Patients only have to be spoken to once when using MPI, and the identifiable proofs and facility information are obtained (Protze et al., 2014). Using MPI to keep patients’ information not only help clinics in sorting them out more easily but also provides effective and exact ways of attending to their patients based on data reflected in MPI. Allocation of patient numbers (referred to as the novel medical record number) helps the medical staff to easily distinguish patients’ records as well as track and carry out cross-referencing.
Although every patient has a unique identifier or special identifier, there are other cases where patients share same identifiers thereby causing possibilities of such patients sharing medical records (Voegler., et al, 2016). Errors resulting from MPI overlays and copies often lead mistaken information thereby posing a threat to the wellbeing of the individuals concerned since there is possibility of accidentally settling for wrong choices of medication due to the shared special identifiers.
One way of solving the errors in MPI is coming up with strategies that can help eliminate the duplicates with a view of giving clinicians access to valid records. By shifting through essential data framework to departmental applications, suppliers can dispose the possible duplicates in the industry. Likewise, doctors and other medical staff should try to recognize any duplicates and find ways of minimizing the risks. For instance, incorporation of more than one HIS MPI can help in venturing into a master patient index thereby ensuring that far-reaching and efficient patients’ records are maintained (Laguna, et al., 2014).
Another possible way of solving the MPI errors is by maintaining high MPI integrity. Since deterministic calculations can be used to join patient records, information such as date of birth, name, gender, and security number can be incorporated to come up with exact records. Besides, probabilistic-based frameworks are crucial for sending of the measurable examinations so to breakdown the required MPI information. Most medical services that focus on the adoption of probabilistic-coordinating calculations to actualize EMPI or MPI utilize controlled frameworks.
Data quality refers to the various attributes and elements of a set of data that shows its ability to satisfy the needs by utilizing its outcomes. This concept argues that the quality of a data set is easily determined when it can easily be used in the basic leadership and arranging. Some of the qualities and attributes of a good quality data include accessibility, accuracy, believability, interpretability, objectivity, and relevancy. Moreover, other attributes like representational consistency, reputation, timelessness, value added, and completeness is important in determining data quality. A data set of good quality should be clear and easy to understand.
The use of appropriate and unbiased language is very important when determining the quality of a given data set. Although there exist various measures of traits that may be useful for a good quality data, the suitable arrangements may vary from one setting to another. There is need for data to be interpretable in the context and dialect to ensure that the goals of collecting the data are met.
Although good quality data is considered the pillars of basic leadership, such arguments are subjected to the auspicious and exact data measurements. However, poor data measurements continue to exist all over the world thereby endangering the lives of many people. Due to the negative consequences of poor quality data, there is need to adopt quality data that can help in examining the extent to which data can help ran projects with aims of improving the wellbeing of people around the world. Furthermore, issues like mortality, fertility, and paternity rate insights can be used gathered to help set important goals like the millennium development goals.
In the context of medical facilities (clinical settings), quality of data is considered to be directly related the quality of services that are provided to the patients (Botha, Botha & Herselman, 2014,). Data quality in clinical settings, therefore, ensures that possibilities of errors due to wrong and poor quality data are eliminated thereby preventing any harm that such errors may cause to the patients or health care service consumers.
Authorization, Consent, and their Implications
According to the HIPAA privacy rules, covered entities are not required to voluntarily obtain consent from patients before use or disclosure of crucial data like PHI (the protected health information) that are related to treatments, health care operations, or payments (Fisher, 2004). However, covered entities that require consent of the patients are also required to set up complete discretion so to design a process that will be beneficial for their needs. Keller et al (2007), argues that consent can be referred to as the act of requesting the users or respondents to use some or whole of the information they provide for your own benefit. Moreover, consent involves obtaining information from participants, who may voluntarily decide not to give the information or allow their information to be used for a given purpose.
Authorization, on the other hand, is considered as a detailed document providing all the covered entities relevant permissions to utilize the PHI for specific uses other than health care operations, payments, or treatments. It may also disclose the parties that are designated by the individuals. Authorization clearly identifies the PHI to be used, people authorized to carry out such disclosure or use such information, purpose of the data required, and expiry dates of the authorization.
Although the privacy rule requires the patient authorization before utilizing some aspect of their information, voluntary consent cannot be treated as a sufficient evidence to warrant the disclosure of a patient’s PHI, unless it’s considered to be part of valid authorization (Fisher, 2004). Authorization gives consent to utilize the data necessary for other uses other normal healthcare operations. The information obtained through such process is instrumental in designing high quality data that are necessary for designing the community wellbeing. Other than issues such as ailments, deaths, and other critical issues, authorization allows the various entities to collect viable information needed to improve the quality of health care. With the existence of secured data elements, restricted exemptions may be beneficial for the scope on an individual who is giving the required authorizations.
Botha, M., Botha, A., & Herselman, M. (2014, December). Data quality challenges: A content analysis in the e-health domain. In Information and Communication Technologies (WICT), 2014 Fourth World Congress on (pp. 107-112). IEEE.
Fisher, C. B. (2004). Informed consent and clinical research involving children and adolescents: Implications of the revised APA ethics code and HIPAA. Journal of Clinical Child and Adolescent Psychology, 33(4), 832-839.
Keller, S., Korkmaz, G., Orr, M., Schroeder, A., & Shipp, S. (2017). The Evolution of Data Quality: Understanding the Transdisciplinary Origins of Data Quality Concepts and Approaches.
Laguna, I., Richards, D. F., Gamblin, T., Schulz, M., & de Supinski, B. R. (2014, September). Evaluating user-level fault tolerance for MPI applications. In Proceedings of the 21st European MPI Users' Group Meeting (p. 57). ACM.
Protze, J., Hilbrich, T., Schulz, M., de Supinski, B. R., Nagel, W. E., & Mueller, M. S. (2014, September). MPI Runtime Error Detection with MUST: A Scalable and Crash-Safe Approach. In Parallel Processing Workshops (ICCPW), 2014 43rd International Conference on (pp. 206-215). IEEE.
Voegler, R., Becker, M. P., Nitsch, A., Miltner, W. H., & Straube, T. (2016). Aberrant network connectivity during error processing in patients with schizophrenia. Journal of psychiatry & neuroscience: JPN, 41(2), E3.
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