Abstract
Management of data integrity is a highly vital role of the chief compliance officer of every organization. It is also my role as the head of the department in the southwest regional hospital group to ensure that the corporate compliance program functions as a sovereign and objective body with the sole purpose of reviewing and evaluating compliance matters that may come up in the organization. It is also the duty of the officer to ensure that all the employees work within the rules and regulations set by the regulatory agencies, that they follow the company’s procedures and policies and that their behavior in the enterprise meets the set codes of conduct. This paper will come up with policies that policies and procedures that ensure data integrity and align them with objectives of the HITECH Act.
Policy and procedures
The first policy that would be advocated for is that data is only collected once with the made readily available to those who have the relevant need with it. The data should not be made accessible to everyone but only those with the authority to access it. Another issue that I would look into is the levels of power and the data accessibility. Only those upper the chain of command should have access to the sensitive data while those with no authority should have restrictions while accessing data. The data should also not be accessible or manipulated for personal/selfish reasons. Those who are found doing this should be prosecuted in the court of law, as there are laws that govern that. When the data is no longer useful to the administration, it should be deleted in a way that recovery difficult. This is because people might retrieve the data for their selfish reasons (Titus, 2013).
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HITECH Act
One of the primary objectives of the HITECH Act is making sure that there is an improvement in the provision of healthcare quality, reduction of medical errors, and an advancement in the delivery of medical care (Data Watch: HITECH Act Reimbursement Plan to Achieve Meaningful EHR Use, 2010). One of the policies that were earlier stated is making data readily available to those that who deem it relevant. This would play a crucial role in eliminating medical errors that often come up. Once a patient’s information is fed into a computer, it should be made available to the staff members who will know the patients’ medical history before treatment is offered. Failure to present this information to the medical staff can lead to small mistakes that could have been avoided had the data been presented when required. This would be effective in the administrative data as it would ensure an improvement in the provision of healthcare services.
As for the medical information, the policy proposed on the restriction of data should come in handy since another objective of the HITECH Act is the promotion of early disease prevention and detection especially about chronicle diseases (Schwartz, Magoulas, & Buntin, 2013). If medical information about an ongoing research falls in the hands of a lower-level employee, they might sell it for personal gain. This can easily be avoided by restricting the data that they have access to.
An individual for their personal gain can steal the financial information. The information should be kept safe from the eyes of the public and other staff members who have nothing to do with financial records. Another objective of the HITECH Act is the reduction of healthcare costs that often result from inefficiency. What causes inefficiency? It is caused by the loss of data in most cases, which could have been stolen for selfish reasons. This can be avoided, and the healthcare cost can be reduced to a level that everybody can afford.
Data architecture models
High-level data model
This model is highly associated with all the attributes that have business significance. As the chief compliance officer, I would recommend that it be used for the financial data. This type of model describes business data from a conceptual viewpoint that is sovereign of any trending realization by actual systems (Liwu Li, 1994). It often comprises of two factions one is a typical UML class model that contains the main data items and most of their relationships. The second faction is the superset of business attributes, including semantics that can be described as a description of the meaning of business attributes, standardized formatting also known as syntax and universal constraints.
Source and consumer model
This is a paramount example since it shows the difference between different realizations that belong to the same data item, how changes are conducted across systems and organizational custodians that are of different data elements. The model focuses on the identification of roles, provenance and additionally the evolution of each data item with the help of the following stereotypes: <<Master>>, whose role is the identification of an agreed source of data, <<uses>> which deals with the identification of significant cross-organizational data usage. This would come in handy with the objectives of the HITECH Act, which call for the communication of different medical facilities. I would recommend that this model is used for medical data that can be shared among different health centers and Also the financial records since it allows one system to use another system with the help of existing interfaces.
References
Data Watch: HITECH Act Reimbursement Plan to Achieve Meaningful EHR Use. (2010). Internal Medicine News, 43(8), 42. http://dx.doi.org/10.1016/s1097-8690(10)70446-7
Liwu Li, (1994). High-level Petri net model of a logic program with negation. IEEE Trans. Knowl. Data Eng., 6(3), 382-395. http://dx.doi.org/10.1109/69.334863
Schwartz, A., Magoulas, R., & Buntin, M. (2013). Tracking Labor Demand with Online Job
Postings: The Case of Health IT Workers and the HITECH Act. Industrial Relations: A Journal of Economy And Society, 52(4), 941-968. http://dx.doi.org/10.1111/irel.12041
Titus, S. (2013). Evaluating U.S. Medical Schools' Efforts to Educate Faculty Researchers on Research Integrity and Research Misconduct Policies and Procedures. Accountability In Research, 21(1), 9-25. http://dx.doi.org/10.1080/08989621.2013.822264
