Implications and Limitations of Research

Implicationsand Limitations of Research

Inthis research paper, it is clear that the total expenses incurred ina hospital, and particularly Virginia Hospitals depend on a number offactors and operations set up to meet objectives of the institution. The relationship between total expenses and predictors or independentvariables can be developed through linear regression if oneindependent variable is considered. However, since the total costdepends on several independent variables, a multiple regression modelis developed that can be used to predict future expenses and makebudgetary allocations that will cover operations of the hospital. The use of statistical data analysis is inevitable in comparing andanalyzing how total expenses in the hospital were affected by othercost variables. The significance of the data can also be tested soas to qualify any decision that the hospital will make.Based onthe regression model developed, a number of actions can be taken tomeet objectives of the hospital and set up professional standards. Services can be improved where indicators show a drop, and new costeffective operational methods implemented. &nbspImproved methods ofcollecting, analyzing, interpreting, and acting on data for specificperformance measures will allow health care professionals to identifywhere systems are falling short, to make corrective adjustments, andto track outcomes [ CITATION HRS14 l 1033 ].

Useof statistical tools in decision making is also important such ascalculation and comparison of means. Setting up databases wouldenhance optimal health care services thereby meeting the objectivesand aims of the institution. Proper communication and advocacy isnecessary too.

Limitationsof this research will be based on accuracy of data collected, anderrors in determining the predictors or independent variables.Indeed, accurate cost estimation is problematic when cost records areincomplete, because censoring could lead to biased estimates ofcosts, unless appropriately accounted in the analysis [ CITATION Gre11 l 1033 ]. Astandardized data collection procedure is essential for successfulplanning and quality improvement of healthcare services in thehospital. &nbspA standardized data process will simplify the task ofquality improvement by allowing you to collect accurate andconsistent data and generate reliable information to act upon. Thepromotion of flawed data by hospital practices, payers, or healthcaresystems can encourage, if not institutionalize, the delivery ofineffective, harmful, or wasteful interventions. The same partiesthat stand to benefit from the data, healthcare professionals, andthe whole healthcare system—may all be harmed [ CITATION Woo99 l 1033 ].

Samplingmay be part of data collection methods and this may not give resultsthat can be applied to the population if the sample is small. Thiswould therefore make conclusions to be unreliable ultimately makingthe cost model also to be unreliable. However, 81 hospitals wereconsidered in this research and the statistical descriptions will bereliable in this case. Variables in the model also do not remainconstant over time hence the model has to be reviewed regularlybefore it is standardized.

Errorscan occur especially when we accept a hypothesis when we shouldactually reject it or reject a hypothesis when we should actuallyaccept it. Such errors may not be easily captured in this researchproject and will therefore be a limitation.

Healthcareinformation technology executives are being tasked with developingand operating solutions that integrate data from a range of patient,clinical, and back office systems to healthcare providers, patients,payers, technology and pharmaceutical companies [ CITATION Woo99 l 1033 ]. Theestimation of total medical cost is not straightforward, particularlywhen the goal of the analysis is to relate costs to a specificpattern of covariates. However, we can overcome such limitations byimplementing proper healthcare information systems in ourinstitutions.

References

Gregori, D., &amp Etal. (2011). Regression models for analyzing costs and their determinants in health care: an introductory review. International Journal for Quality in Healthcare Vol.23/3 , 331-341.

HRSA. (2014). Managing data for performance improvement. Retrieved December 16, 2014, from Health Resorces and Services Administration web site: http://www.hrsa.gov/quality/toolbox/methodology/performanceimprovement/index.html

Woolf, S. H. (1999). Potential benefits, limitations, and harms of clinical guidelines. British Medical Journal 318(7182) , 527–530.