Health Care Data Quality
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Data quality is very crucial especially in the health sector where life must be accounted for. Health systems ensure the society remains healthy and without proper data management there will be major setbacks in implementing future plans. The learning objectives include; being able to weigh against and contrast the various definitions in health care; to be describe the major external and internal health care information in the health systems; to refer to specific examples in the health systems; understanding patient records i.e. their content and usage; to track the health of patients health in each encounter of ailment.
Data versus Information
The difference between data and information is that is that information is processed data. Health care data are raw facts that are collected and stored as character, symbols, measurement, words or statistics. Raw data is usually not important in making decisions therefore they need to be processed to bring out trends and meaning to be applied in decision making.
Problems with poor quality date
The problems of having poor quality data is that it can affect the purposes of maintaining patient records (Sieber, p.57). At the health centre there will be reduced health quality f patients, poor communication among the patients and health practitioners, documentation problems, lower revenue due to reimbursement problems, and ineffectiveness in the health center (Di Lima, et al, p.76).
Documentation is also very crucial just like in the courts. Documentation can help in tracking all the progresses of the patient and the drugs and ailments that have been treated. Any errors are also likely to correct if there is proper documentation (Rizzo, & Sindelar, p.476).
Data and Information Quality
For proper health care decisions to be made high quality data and information must be ensured at all times. Health experts must aim at achieving high quality information through establishing standards that are recognized. Although there is no universally recognized standard the need for quality data for specific use should propel high standards with minimal errors (Naeim, et al, p.188). Health data quality has been increased over the years and there are many opportunities due to adoption of technology in most health centers.
There are opportunities of integrating data from different heath center departments. With such integration management of data and information will be much easy and professionally done. For example personal data of patients would be integrated with treatment details as well as the medical staff who have attended them and billing details. Therefore, generation of customized report which assists in decision making will be much accurate, reliable and timely.
However, adoption of technology in data and information management is faced by two man challenges; data and information security and skills. The technology keeps on changing much faster than how the medical staffs are upgrading their skills on how to use and adopt the technology. Therefore, there has been delay on implementation of most applications effectively due to lack of skills. Data and information security have been a challenge because all details are being stored in the same systems (Dick and William, p.120). Hence, any unauthorized access that is malicious might change or damage the data. In case the system fails, health facilities operations might be grounded therefore there is need to invest in data/information recovery systems to ensure incase of such failure the institutions would proceed on with their operations.
Medical Records Institute (MRI) and the American Health Information Management Association (AHIMA) as association that have published data quality management tool.
Data granularity/atomicity indicates that each data element is atomic and thus can not be subdivided. Data precision means that the data must be close to the actual standards of measurement and is mostly linked to numerical data. Data relevancy means that the data must serve and be relevant to the purpose it is collected for. Data timeliness means that data must be availed at the appropriate time.
Testing use of Information Technology (IT)
IT in data centers help in improving data quality, processing and inference. The uses of electronic magnetic records (EMR) have been more effective in the above advantages and are also use in storing and retrieving data (Flanagan, p.331). In structured data IT has improved comprehensiveness, relevance, precision, accuracy and consistency.
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