Wednesday, May 6, 2020

Using Healthcare Data For Decision Making

Question: Discuss about the Using Healthcare Data For Decision Making. Answer: Introduction The information in the healthcare profession is consistently monitored in terms of data quality, coding and documentation so as to provide an assurance of compliance and reimbursement of the healthcare standards. As the cost to healthcare rise it causes an increase to consumer needs, and also emergence of the new consumer needs. It is vital for the industrial healthcare profession to ensure there is an evaluation of trends and development of healthcare systems. The systems are supposed to be efficient and effective as well as affordable both for the healthcare providers and the consumers. Good decision making should be based on the accuracy of quality information. The healthcare professionals are diligently making sure that the information it is critical for the future of the healthcare providers. The healthcare providers generate and collect data for making good decisions. There is an increase of data collection which requires an automatic way for it to be extracted, when it is needed (Hesse, et al. 2011). When using data for decision making it is very possible to find a clear and precise decision which is useful for healthcare departments. Using data for decision making is very useful in controlling consumers limitations such as error and subjectivity due to fatigue, and also provision of clear indications for decision making process. The essence of using data in the healthcare sector is an indication for models, relations and patterns which provide support for the process of decision making such as treatment planning and diagnoses. These models are predictive, and integrated into the hospitals information systems as models for decision making, which is a way of reducing subjectivity. Therefore, this paper will discuss the method of using the healthcare data for decision making, the results or the findings of the method, discussion and recommendation behind the method and conclusion. Background The research analysis is based on data researched and presented by Australian UTS Online Public Hospital located in New South Wales. The aim of report is to use healthcare data in addressing issues related to diabetes Mellitus and come up with proper decision (Assal, 2013). This disease is known as diabetes which involves a group of metabolic diseases that have a high blood sugar level. Using healthcare data for decision making is important when giving a report on how diabetes has spread in Australia (Hibbard, Slovic and Jewett, 2009). Currently in Australia, there is no national healthcare data for measuring and monitoring the trends of diabetes mellitus. The data and information produced is based on the report of Australian public Hospital. This hospital addresses issues related to diabetes mellitus. Between 1989-90 the percentage of those reported suffering from diabetes mellitus doubled from 1.5% to 4.2%, however, the rate remained constant in the year 2007-2008. Therefore, in this paper the analysis will be based on the UTS hospital data which will help the Australia to totally eradicate diabetes mellitus (Australian Institute of Health and Welfare, 2009). The online UTS report released early 2016 indicated that the diets of the Australians are going downward, as junk food was reported the main cause of diabetes mellitus (Gracey and King, 2009). This raised the countrys score up 10 points; this would help the country moderate against growing lifestyle diseases such as heart diseases and regulate increased rate of obesity (Kopelman, 2013) Current Prevalence and incidence of diabetic mellitus in Australia Current admission and the length of stay Table 1: Showing admissions and length of stay Demographic With Diabetes Without diabetes No. of those participated 23, 779 239, 703 Admissions Total no. of admissions 16,692 107,343 Daily 10,231 75,197 One day 6,460 32,145 No. of those admitted All 7,807=32.8 57,970 =24.2 First 1 event 1,034 =4.3 7,752 =3.2 Principle cause 710 =3.0 - Diabetes as ACSC2 1,744 =7.3 - The above table 1 shows the rate of admission diabetic mellitus in Australia was 9.0% of23,779. The rates for admission of the participants, 454.48 and 631.3 every 1000 and total mean of the length of stay was 7.1 and 8.2 for those without and with respectively. Both the patient the risk was related to household income, gender, BMI, age, smoking and physical activities. In current trend Diabetes is an epidemic disease, it is the biggest challenge faced by Australian health institutes (Zimmet, Alberti and Shaw, 2015). This disease is one of the chronic diseases in Australia such as cancer and heart diseases that are growing very fast. All the types of diabetes are increasing in frequency, for example type 1 diabetes accounts for about 10% increase, type 2 accounts for 85% and gestation diabetes is also increasing in pregnant women (Moses, et al. 2011) ii) Approaches used in decreasing the length of stay Patients with crucial problems or those who do not respond well in terms of glucose control to the initial treatment regime should get referred to special diabetic staff. In order to reduce the length of stay in hospital of diabetic patients, there has to be specific protocols drawn for those patients admitted due to acute diabetes. The diabetic patients undergo basic assessment which is done by the nurses (De Berardis, et al. 2012). The assessment is important, because those patients who have stable diabetes normally are not seen unless their situations start to deteriorate. This allows the nurses to take care of the weak patients and not stable dietetically. This reduces their time of stay in the hospitals. However, where there is consistency among the specialists it clearly suggests that the post itself will bare good effects. The objective of the post is to ensure that diabetic patients have good control during their stay in the hospital (Aro, 2013). As a result of this effect, there is education such as dietary advice, for the diabetic patients according to every patient needs, with appropriate equipments (Holmes, et al. 2014). Data analysis plan of the UTS Below is the data analysis for the Australian healthcare for decision making. The tables clearly illustrates individuals characteristics relevant to diabetes mellitus Table 2: UTS data analysis of the profile surgical patients profile Diabetes No diabetes participants 23,779 % 239,703 % Gender Male 13,393 56.3 108,589 45.3 Female 10,386 43.7 131,114 54.7 Age group 45-59 6,698 28.2 115,733 48.3 60-74 11,143 46.9 86,102 35.9 75 5,935 25.0 37,850 15.8 Table 2 above summarizes that those who had diabetes were male who are aged between 65 to 74 years. The men were born in a foreign country, not completed the 10th year of schooling, they live a commune which is disadvantaged and have domestic income of $20,000 when compared to those without. Those without were likely to be less obese, heart disease, depression. iii) Diagnosis of secondary and primary Table 3: Showing secondary and primary diagnoses profile With Diabetes without diabetes No. of participants 23, 779 % 239, 703 % Gender of participants Male participants 13, 393 56.3 108, 589 45.3 Female participants 10, 386 43.7 131, 114 54.7 Age of participants 45 to 59 6, 698 28.2 115, 733 48.3 60 to 74 11, 143 46.9 86, 102 35.9 75 5,935 25.0 37, 850 15.8 The above table 3 shows the rate of admission diabetic mellitus in Australia was 9.0% of 23,779. The admission rates for the participants were 454.48 and 631.3 per 1000 and total mean of the length of stay was 7.1 and 8.2 for those without and with respectively. Both the patient the risk was related to the household income, age, BMI, smoking, gender, physical activities and health. Findings The UTS found that the risk for hospitalization was as a result of smoking, gender, age, health, physical activity, wellbeing (Aro, et al. 2013). The risk was attenuated those who are older and had diabetes and they were likely to hypertension, obese, hyperlipidaemia, this enhanced those participants with diabetes mellitus were likely men, current smokers or those had depression. Dormant diabetes mellitus in adults slowly may develop like type 1 diabetes. According to the research done by healthcare providers who used healthcare data for decision making, diagnosis of diabetes mellitus mostly occurs after a period of 30 years. The beta cells are destroyed by immunity of the body. During diagnoses, those persons with autoimmune diabetes produce their own insulin, but most of them require insulin pumps to keep the blood sugar at the normal level. Discussion The UTS online hospital in Australia used healthcare data for decision making. The hospital observed that married diabetic patients had lower rate of metabolic syndrome and BMI as compared to widows or single patients. Moreover, those who stayed together with their spouses were 58% likely not to develop diabetic syndrome and 50% less to become overweight (Australian Institute of Health and Welfare, 2009) The finding existed even after there was an adjustment for diabetic duration, sex and age. The person who is in a state of singlehood risked being overweight, as result of metabolism especially in male patients (Yoshinobu, 2010, 2011). The finding provided a suggestion that there should be a social health care employed to help those single diabetic patients suffering from type 2 diabetes mellitus. This could help them understand how manage their own bodies well. According to the researchers, it was noted that there was lack of information relating to relationships between the spouses and the patients, and this was counted as possible confounder. Similarly, there were cultural differences according to this Australian study which could negatively affect the Australian population with this kind of diabetes. Using healthcare data for decision making is very important especially for those patients suffering from diabetic mellitus. According to the finding of Australian UTS online hospital, Diabetes mellitus is as a result of destruction of insulin producing cells called beta cells(Gracey and King, 2009).. Therefore, the unmarried women or widows according to the finding have a high rate of insulin cells destruction as compared to married men. The women who are prone to diabetic mellitus are severally likely to undergo surgery. Therefore, using healthcare data for decision making prevents more patients to under acute diabetes attack. When it comes to diabetic mellitus, the immune system is the one that protects the body from being infected by destroying viruses, bacteria and other harmful substances. But when it comes to the autoimmune diseases, cells of the body are attacked by the immune system. In type 1 diabetes mellitus, beta cell destruction takes place for quite some years, but its symptoms takes a short time to develop (Yoshinobu, 2010, 2011). According to the research done by the Australia UTS Online Hospital, type 1 diabetes mostly occurs in young adults and children, though it appears at any age. Time ago, type 1 diabetes was known as juvenile diabetes mellitus. Conclusion Using healthcare data for decision making contributes a lot to the rise of clear information or report concerning diabetes mellitus. It is important for the detection of the diabetes mellitus, improved survival of those people who have diabetes and the increase in public awareness about diabetes disease. Healthcare data for decision making is very important as discussed below (Heisler, et al. 2010) which include: The data tends to address medical issues. This is from different aspects of healthcare systems or departments such as pharmacy or radiology. The data is expected from all the healthcare organizations (Thorne, 2014).The data is aggregated into single information or a central system like warehouse for enterprise data. This makes it easier to access the data and makes it actionable. There is complexity in the data since as observed by the healthcare providers. The research done says that healthcare data for decision making has been in existence for quite some time and therefore scrubbed and standardized. But such type of data is referred to as incomplete data. Data from EMRs gives a more complete picture of the patients diabetes story (Tunis, Stryer and Clancy, 2008). Quality data analysis is one of the objectives of the healthcare institutions, though variability of data makes it a challenge to the healthcare sector.Finally, reporting and regulatory requirements are always increasing and evolving. Therefore, Healthcare departments need information which is quality so as to make a precise decision (Eichler et al. 2009). This will ensure transparency in providing information to the public such as pricing information. Therefore, healthcare data is an important factor and is it has advantages to the healthcare sector. The data is helpful especially in the making of decisions that are of great importance both for the consumers and nurse staff. The healthcare sector should always rely on the healthcare data while making decisions. References Aro, S., Kangas, T., Reunanen, A., Salinto, M and Koivisto, V., 2013. Hospital use among diabetic patients and the general population. Diabetes Care, 17(11), pp.1320-1329. Assal, J.P. and Groop, L., 2013. Definition, diagnosis and classification of diabetes mellitus and its complications. World Health Organization, pp.1-65. Australian Institute of Health and Welfare, 2009. Diabetes Prevalence in Australia: An Assessment of National Data Sources. De Berardis, G., DEttorre, A., Graziano, G., Lucisano, G., Pellegrini, F., Cammarota, S., Citarella, A., Germinario, C.A., Lepore, V., Menditto, E. and Nicolosi, A., 2012. The burden of hospitalization related to diabetes mellitus: a population-based study. Nutrition, Metabolism and Cardiovascular Diseases, 22(7), pp.605-612. Eichler, H.G., Kong, S.X., Gerth, W.C., Mavros, P. and Jnsson, B., 2009. Use of Costà ¢Ã¢â€š ¬Ã‚ Effectiveness Analysis in Healthà ¢Ã¢â€š ¬Ã‚ Care Resource Allocation Decisionà ¢Ã¢â€š ¬Ã‚ Making: How Are Costà ¢Ã¢â€š ¬Ã‚ Effectiveness Thresholds Expected to Emerge?. Value in health, 7(5), pp.518-528. Gracey, M. and King, M., 2009. Indigenous health part 1: determinants and disease patterns. The Lancet, 374 (9683), pp.65-75. Heisler, M., Bouknight, R.R., Hayward, R.A., Smith, D.M. and Kerr, E.A., 2010. The relative importance of physician communication, participatory decision making, and patient understanding in diabetes selfà ¢Ã¢â€š ¬Ã‚ management. Journal of general internal medicine, 17(4), pp.243-252. Hesse, B.W., Nelson, D.E., Kreps, G.L., Croyle, R.T., Arora, N.K., Rimer, B.K. and Viswanath, K., 2011. Trust and sources of health information: the impact of the Internet and its implications for health care providers: findings from the first Health Information National Trends Survey. Archives of internal medicine, 165(22), pp.2618-2624. Hibbard, J.H., Slovic, P. and Jewett, J.J., 2009. Informing consumer decisions in health care: implications from decisionà ¢Ã¢â€š ¬Ã‚ making research. Milbank Quarterly, 75(3), pp.395-414. Holmes-Rovner, M., Kroll, J., Schmitt, N., Rovner, D.R., Breer, M.L., Rothert, M.L., Padonu, G. and Talarczyk, G., 2014. Patient satisfaction with health care decisions the satisfaction with decision scale. Medical Decision Making, 16(1), pp.58-64. Jansen, J.P., Fleurence, R., Devine, B., Itzler, R., Barrett, A., Hawkins, N., Lee, K., Boersma, C., Annemans, L. and Cappelleri, J.C., 2011. Interpreting indirect treatment comparisons and network meta-analysis for health-care decision making: report of the ISPOR Task Force on Indirect Treatment Comparisons Good Research Practices: part 1. Value in Health, 14(4), pp.417-428. Kopelman, P.G., 2013. Obesity as a medical problem. Nature, 404(6778), pp.635-643. Moses, R.G., Morris, G.J., Petocz, P., San Gil, F. and Garg, D., 2011. The impact of potential new diagnostic criteria on the prevalence of gestational diabetes mellitus in Australia. Medical Journal of Australia, 194(7), p.338. Thorne, S., 2014. Data analysis in qualitative research. Evidence based nursing, 3(3), pp.68-70. Tunis, S.R., Stryer, D.B. and Clancy, C.M., 2008. Practical clinical trials: increasing the value of clinical research for decision making in clinical and health policy. Jama, 290(12), pp.1624-1632. Tuomilehto, J., Lindstrm, J., Eriksson, J.G., Valle, T.T., Hmlinen, H., Ilanne-Parikka, P., Keinnen-Kiukaanniemi, S., Laakso, M., Louheranta, A., Rastas, M. and Salminen, V., 2009. Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. New England Journal of Medicine, 344(18), pp.1343-1350. Yoshinobu K, 2011.American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes care, 33(Supplement 1), pp.S62-S69. Yoshinobu K., 2010.American Diabetes Association,. Diagnosis and classification of diabetes mellitus. Diabetes care, 29(1), p.S43. Zimmet, P., Alberti, K.G.M.M. and Shaw, J., 2015. Global and societal implications of the diabetes epidemic. Nature, 414(6865), pp.782-787.

No comments:

Post a Comment

Note: Only a member of this blog may post a comment.