Clinical Doc. Improvement
Running head: MODERNIZING HEALTH INFORMATION INFRASTRUCTURE 0 Modernizing Health Information Infrastructure Karese Holmes HIMS 655 Health Data Management Modernising Health Information Infrastructure According to Davoudi et al., (2015) healthcare leaders experience challenges such as payment reform, exchange of health information, among others. Ideally, the nexus in the challenges is to ensure that data remains a trusted source that can be exchanged, shared, and accessed with ease. The American Health Information Management Association offers the basis of information and data governance through some fundamental principles. This principle includes accountability, transparency, integrity, protection, compliance, availability, retention, and disposition. The principles are critical for the data quality management model. Data quality management refers to the business process that guarantees the integrity of organisation information during the analysis, warehousing, application, and collection processes. The healthcare industry has some task to ensure a robust objective of the healthcare standards. Significant limitation of the models Data should apply security controls to offer data protection to guarantee data quality management in the American Health Information Management Association. Ideally, data needs to be protected in backup environments and storage. Additionally, data needs tracking using confidential audit trail. Besides, the entire data should ensure that the entire data scope is gathered while documenting the resulting limitations (Davoudi et al., 2015). The Canadian Institute for Health Information has the primary goal of ensuring that the framework for data quality management maintains and achieves a high degree of quality and meets the requirements for data users. Notwithstanding the publication of the national pollutant release inventory is mandatory as enshrined under the Environmental Protection Act in Canada (“Environment and Climate Change Canada”, 2016). Recommendation for submitting AHIMA’s global health workforce (AHIMA Public Policy Statement (2012) reveals that the American Health Information Management Association recommends that the entire healthcare entities should satisfy the compliance for implementing healthcare standards. Therefore, AHIMA should ensure that the health department and human services reconfirm its obligation to employ the data quality management standards. Additionally, the AHIMA should include its entire data in the electronic health records. This concept will ensure that the entire Americans benefit from the abundant improvements in the classification of the healthcare records. AHIMA needs accurate healthcare information to support national healthcare initiatives including value-based purchases, patient safety, and quality measurements. It is essential that AHIMA should consider the healthcare transition as fundamental; to the healthcare providers in the United States. Concerns about use and development of data quality checklists Data quality checklists are offered as a recommended tool to complete the data quality assessment. In essence, data quality checklists should use a different tool for documenting and conducting data quality assessments. Data quality checklists are essential in assessing the entire aspects of data quality. Besides, it offers a convenient platform for documenting the data quality assessment findings. However, the data quality checklists should ensure that they recognise the purpose of assessing the performance indicator (Bruce, 2014). Additionally, the data quality checklists have to ensure that it records the titles and names of the individuals during the assessment process. In conclusion, healthcare leaders continue to experience challenges in the healthcare sector. AHIMA offers the basis for information governance through the use of fundamental principles. The fundamental principles for data quality management include accountability, transparency, integrity, protection, compliance, availability, retention, and disposition. AHIMA should track its data using the confidential audit trail. Conversely, the Canadian Health Institute is limited to ensuring that its data quality management results in a high degree of quality to meet the requirements for its entire data usage. AHIMA is recommended to ensure that its entire healthcare entities satisfy the compliance in implementing the healthcare standards. Data Quality Management Model Data Quality Characteristics Data Quality Measue(s): YES NO Comments   Patient name is list correctly on all documents               Patient DOB/SNN (LAST 4) listed correctly on all documents               Financial Information listed               Consent/Authorizations forms are signed               Current Treatment History               Patient Progress Notes               Physician orders and prescriptions               Lab reports               Medication List               HIPAA Notice/Patient Privacy Rights             References American Health Information Management Association Public Policy Statement. (2012). Retrieved from http://www.mnhima.org/ICD10Page/ICD10PolicyFinal.pdf Bruce, K. (2014). Field Guide for Data Quality Management. Washington, DC. Retrieved from http://www.pactworld.org/sites/default/files/DQM%20Manual_FINAL_November%2020 14.pdf Davoudi, S., Dooling, J., Glondys, B., Jones, T., Kadlec, L., & Overgaard, S. et al. (2015). Data Quality Management Model (2015 Update). Journal of AHIMA, 86(10), expanded web version-. Retrieved from http://bok.ahima.org/doc?oid=107773#.V_GvvfArKUk