Assessment of Healthcare Performance - Literature review Example

What effect do missing, incomplete or incorrect data have on the assessment of healthcare performance? Healthcare performance is a measure of how effectively the goals set by all concerned parties in the healthcare system are met. It has been suggested that there are four stages to the measurement of performance management: conceptualization, selection and development of measures, data collection and processing and reporting and the use of results. It is within this framework that goals are set and whether these goals are met is a good indicator of the performance of a certain healthcare facility (Adair et al, 2006). A key element of this healthcare performance is the effect of missing, inaccurate and incorrect data in medical records. The rest of this essay will attempt to focus on this aspect of healthcare performance and try and provide an insight into exactly what the effects are. To measure healthcare performance, a healthcare protocol should first be penned down. This should cover the objectives, information to be collected and context and procedures to be used amongst other things. These protocols are to be seen as ‘screening devices’ to measure performance (Bird et al, 2005). Examples of factors used to measure healthcare performance are: regulatory inspections, surveys of consumer experiences, third-party assessments, statistical indicators and internal assessments. Most places have compulsory inspections at periodic intervals and these can include areas of fire, hygiene, radiation, medical devices, medicines, infection control and blood transfusions. These regulatory inspections have, however, been criticized as they conform to the minimum required standard. Standardized surveys are used to gauge what public opinion is to the entire medical experience. These provide insight into what the patients thought of the system and what they require in the future. Third party assessments are supposed to provide an unbiased opinion by peers of factors like hospital laboratories. Statistical indictors are necessary to suggest issues relating to performance management. Standardization is a key element of this process, both within hospitals and between hospitals (Shaw, 2003). Internal assessments have also been recommended to take place in every institution every so couple of years. A key element of health care is the maintenance of good administrative data. Inaccuracies can adversely affect patients and be extremely costly. A study carried out by Peabody et al, 2004 attempted to determine exactly how accurate both the primary and secondary diagnoses was as recoded in administration data. Diagnosis is an integral part of a patient’s care and it was this aspect of care that was focused on in this study. Diagnosis data are collected when the patient visits the healthcare facility and later transferred to other records. Actors masquerading as patients were used in this study so as to be able to follow the results. Results were surprising in that they found substantial errors are commonplace. The primary diagnosis was inaccurate for a startling 43% of cases, many of the forms filled were lost or missing and in 22% of cases, data was entered incorrectly. The secondary diagnosis more accurate though there were still cases of inaccurately filled forms (Peabody et al, 2004). However, while errors in data management do occur, when the risk adjusted hospital mortality rates were looked at and compared with differences in quality of care due to healthcare performance, there was no link between a lower quality of care and mortality. This indicates that while errors do occur, they are not ones that are fatal (Pitches et al, 2007). The consequences of incomplete, inaccurate and missing data have been divided into two issues by Peabody et al, 2004: administrative and financial issues and policy level issues. Administrative data sets are often used as an indictor of outcomes of treatment and the standard of quality of healthcare being offered. These inaccuracies would misrepresent these evaluations and affect patients in that they may not be informed of potential new treatments for their condition. Financial issues may include that insurance companies will not fully reimburse patients what they are owed due to under-reporting of the condition (Peabody et al, 2004). In terms of inaccuracies, missing and incomplete data affecting policy research, the allocation of funds regarding the various sectors of healthcare will be compromised. This is especially true if a certain condition is underreported (Peabody et al, 2004). It must be noted that figuring out a system to maintain accurate records is especially important due to the automation of many systems (Peabody et al, 2004). Bolsin et al, 2005 has reported that the medical field has ‘a hidden curriculum’ where by errors are often misreported and there is a reluctance to monitor performance. A step in the right direction would be to have a system that is linked to a data collection facility which links to a password protected site that is accessible by both the clinician and the patient .The proposed model would include regulation through individual observation, at the organizational level and also a further regulation at the state or national level. The patient would have the opportunity to microregulate their own results and keep track of what is going on (Bolsin et al, 2005). It is more likely that errors will be picked up quickly if the patient has access to their own results. In addition to the making sure the data on record is accurate, there is also another issue that tends to be overlooked. This is the purging of data that is no longer of use. While many healthcare facilities looked at suggested that they did have policies and procedures in place to remove outdated data (61%), not every facility had these regulations in place. The removal of unneeded data results in an overall maintenance of up-to-date information that presents an accurate view of the conditions affecting the population at any one particular time (Lorence, 2003). Minor things like the change of an address the change of a last name for female patients are often ignored when updating data. In addition, information regarding insurance companies is often incomplete and this causes delays in payment or even some cases of patients not being reimbursed for what they are entitled to (Lorence, 2003). Most of healthcare claims nowadays are processed through automated systems which deal with a huge volume of data. The information is entered into the system by 3 ways, data received on paper is entered in by clerks, optical character readers are used or the data is entered in an electronic form directly provided by the company (Lorence, 2003). Whilst the advantages of these methods are many, it is usually an automated system that decides whether a claim should be made and any inaccuracy in the collection of the data may result in non-payment or a significant delay in processing a claim by an insurance company (Lorence, 2003). A report published by the Global GDSN Pilot report, 2008, which is suggesting a system similar to the one recommended by Lorence, 2003, revealed startling facts on costs that burden the healthcare system as a results of misrepresented data. $2-5 billion American dollars a year was reported to be spent on data corrections and inefficiencies along with 24%-30% of its administration time being spent on this task. This seems to be in agreement with results obtained by Peabody et al, 2004 in the study mentioned earlier in this discussion. In addition, there were seen to be errors in hospital product information and buyer information is often inaccurate. This leads to the buyers sourcing outdated data and spending much more than what is needed. A whopping 30% of hospital product information was inaccurate and each transaction that resulted from this inaccurate data cost the healthcare system an average of $60-80 dollars. This seems to be the scenario that was demonstrated by Lorence, 2003. Furthermore, 60% of invoices generated in the healthcare supply chain were seen to have some sort of error on them, a result demonstrated by Peabody et al, 2004. Every single invoice error can cost between $40-400 to correct. This misrepresented data also tends to up the supply costs from 3-5% (Global GDSN Pilot report, 2008). The pilot study by Global GDSN Healthcare, 2008 in the US seems to indicate that a system suggested would vastly reduce the numbers of inaccuracies This pilot study was planned to be expanded to Australia and other countries to investigate whether the results would mirror the ones seen in the US study. The report included a guideline of the steps that need to be implemented to get this database started as well as some mandatory attributes that must be in place. In conclusion, it can be said that the quality of healthcare performance is essential to the citizens of a country. In the US and many other countries in the Western world, it appears that whilst there are systems in place to measure healthcare performance, there are not too many examples of change resulting from this screening. An area that needs to have some attention focused on it is the data that is maintained by a medical facility. Administration errors in entering the data cost the industry a large amount of money and can cause delays in the patient receiving treatment and/ or their insurance claims. In a world where numerous people do not have access to healthcare due to financial constraints, it seems shameful to see that billions of dollars are being spent a year just to correct mistakes that should have been made in the first place. There has been a recommendation to introduce a password protected global database and both the medical health staff and the patient would have access to. This has been successful in a pilot study and if the system is implemented will hopefully reduce the number of errors in data and spare patients and governments both financial and personal losses. Bibliography Adair, Carole E.; Simpson, Elizabeth. & Casebeer, Ann L. 2006. Performance Measurement in Healthcare: Part II – State of the Science Findings by Stage of the Performance Measurement Process. Healthcare Policy. Vol. 2. Pp 56-78. Bird, Sheila M.; Cox, David.; Farewell, Vern T.; Goldstein, Harvey ;Holt, Tim. & Smith, Peter C. 2005. Performance indicators: good, bad, and ugly. J. R. Statist. Soc. A . Vol.168 (1). pp.1–27. Bolsin, Stephen.; Patrick, Andrew.; Colson, Mark.; Creatie, Bernie. & Freestone, Liadane. 2005. New technology to enable personal monitoring and incident reporting can transform professional culture: the potential to favourably impact the future of health care. Journal of Evaluation in Clinical Practice.Vol. 11 (5). pp. 499–506. Global GDSN Pilot Report. Synchronizing Product Data in Healthcare.2008. GS1 AISBL. Lorence, Daniel. 2003. Measuring Disparities in Information Capture Timeliness Across Healthcare Settings: Effects on Data Quality. Journal of Medical Systems. Vol. 27(5).pp. 425-433. Peabody, John W.; Luck, Jeff.; Jain, Sharad.; Bertenthal, Dan. & Glassman, Peter. 2004. Assessing the Accuracy of Administrative Data in Health Information Systems. Medical Care. Vol. 42(11). pp.1066-1072. Pitches, David W.; Mohammed, Mohammed A. & Lilford, Richard J. 2007. What is the empirical evidence that hospitals with higher-risk adjusted mortality rates provide poorer quality care? A systematic review of the literature. BMC Health Services Research. Vol.7(91). Shaw C. 2003. How can hospital performance be measured and monitored? Copenhagen, WHO Regional Office for Europe (Health Evidence Network report). Read More