Discuss about the Healthcare Data for Decision Making.
The concerns of the Director for Nursing were right because, as she explained, the hospital should rethink its billing strategies and adopt a more beneficial alternative. This is a clear indication that the hospital has been benefiting from its existing funding approaches. Despite advocating for a fair charge on all the patients based on the average length of stay at the facility; casemix-based is not a good funding approach. It is inappropriate and should not be used for the old patients targeted by the facility (Seah, Cheong & Anstey 2013). Because it cannot suit these patients who usually stay longer at the hospital because of the complex nature of their aging conditions, casemix-based approach has resulted into losses.
Because of this, the management of UTS Hospital should adopt the use of per diem funding. Per Diem is better than the casemix-based funding approach because it emphasizes the need of billing the patients based on the length of stay at the facility. Per Diem approach can enable the hospital to prevent any unnecessary losses that might be suffered as a result of classifying all patients into a single group (Curtis, Lam, Mitchell, Dickson & McDonnell 2014). It will increase the hospital’s income as all the patients will be charged on the services rendered during the entire period of stay. In fact, Per Diem funding is recommended because the facility serves elderly patients who, unlike other younger patients, are likely to stay longer at the hospital because they have complex needs that require much attention by the health care providers at the facility (Hanning & Predl 2015). It is therefore recommended that the management should consider re-introducing the Per Diem approach because it is far much better than the casemix-based funding approach that is to blame for the hospital’s poor performance.
As an organization, UTS Hospital relies on the services of coders to correctly bill its patients so as to get the revenue to rely on in remunerating its staff, meeting its expenses and running its day-to-day operations. However, as the Chief Information Officer said, the hospital has been showing dismal performance because of the coding issues like inaccuracy that has negatively impacted on its billing processes and revenue collection (Ali, Guy, Wand, Read, Regan, Grulich, Fairley & Donovan 2013). The coding errors at the hospital were caused by many factors.
First, the coding officers did not have access to adequate health records to rely on while billing the patients. For a proper coding, the clinical coders should have enough data on the patients’ billing information such as billing procedures. However, without such documentation, the coders are likely to make errors that can in turn be costly for the hospital. This explains why the coding of the ICD-10-AMdiagnoses was poorly done. The other cause for coding inaccuracy is lack of proper training on coding procedures (de Lusignan, Sun, Pearce, Farmer, Stevens & Jones 2014). A clinical coder who is not adequately trained, and lacks information on medical terminologies and updated information on diseases and classification systems is likely to err. Lastly, the coding errors can be prompted by poor coordination of employees. Since coding is a complex process, it cannot be properly done is all the stakeholders including insurance firms, epidemiologists, nurses, physicians, accountants and coders work as a team.
To resolve the challenge of poor coding, the hospital should employ well-trained, competent and experienced coders. At the same time, all coders should be constantly educated, mentored and given in-house trainings on the latest changes in classification systems and coding techniques. Besides, the coding staff should adopt a multidisciplinary approach and collaborate with physicians, nurses, and insurance companies (Burns, Rigby, Mamidanna, Bottle, Aylin, Ziprin & Faiz 2012). Optimal coding results can only be obtained if the coders do not work in isolation, but cooperate and consult with one another.
The information given by the Chief Financial Officer (CFO) is true. The CFO is right because the classification of UTS Hospital as an AR-DRG had nothing to do with the poor performance registered in the recent fiscal periods. It is true that, as he said, that the problem was to be blamed on the average stay of patients. Actually, the averaging measurement system is not the best alternative because it does not cater for individual differences (Hall 2015). It simply generalizes subjects assuming that having similar demographic features makes them identical.
As a facility that mainly targets the gaining populations, UTS Hospital will lose a great deal if it continues using average for the measure of the central tendency. The average length of stay does not apply to the elderly patients because they are likely to stay longer at the facility. The conditions of the elderly patients make them more complicated. Unlike their younger counterparts, the elderly patients are likely to develop multimorbidity conditions that further complicate their health (Lichtenberg 20130. At the same time, they are prone to mental illness and have a higher rate of non-compliance to medication hence forcing them to require more attention and a longer stay at the hospital. This is why the hospital will lose a lot of money if the management does not refrain from using the average system.
Ali, H., Guy, R.J., Wand, H., Read, T.R., Regan, D.G., Grulich, A.E., Fairley, C.K. & Donovan, B., 2013. Decline in in-patient treatments of genital warts among young Australians following the national HPV vaccination program. BMC infectious diseases, 13(1), p.1.
Burns, E.M., Rigby, E., Mamidanna, R., Bottle, A., Aylin, P., Ziprin, P. & Faiz, O.D., 2012. Systematic review of discharge coding accuracy. Journal of public health, 34(1), pp.138-148.
Curtis, K., Lam, M., Mitchell, R., Dickson, C. and McDonnell, K., 2014. Major trauma: the unseen financial burden to trauma centres, a descriptive multicentre analysis. Australian Health Review, 38(1), pp.30-37.
de Lusignan, S., Sun, B., Pearce, C., Farmer, C., Stevens, P. & Jones, S., 2014. Coding errors in an analysis of the impact of pay-for-performance on the care for long-term cardiovascular disease: a case study. Journal of Innovation in Health Informatics, 21(2), pp.92-101.
Hall, J., 2015. Australian health care—the challenge of reform in a fragmented system. New England Journal of Medicine, 373(6), pp.493-497.
Hanning, B. & Predl, N., 2015. New activity-based funding model for Australian private sector overnight rehabilitation cases: the rehabilitation Australian National Sub-Acute and Non-Acute Patient (AN-SNAP) model. Australian Health Review, 39(4), pp.365-369.
Lichtenberg, F.R., 2013. The impact of therapeutic procedure innovation on hospital patient longevity: evidence from Western Australia, 2000–2007. Social Science & Medicine, 77, pp.50-59.
Seah, D.S., Cheong, T.Z. & Anstey, M.H., 2013. The hidden cost of private health insurance in Australia. Australian Health Review, 37(1), pp.1-3.