British Journal of Medicine and Medical Research, ISSN: 2231-0614,Vol.: 20, Issue.: 4
Information Asymmetry in Financial Forecasting within Healthcare and Simple Methods to Overcome this Deficiency
Neeraj Beeknoo1* and Rodney P. Jones2 1King's College Hospital, London, UK. 2Healthcare Analysis and Forecasting, Worcester, UK.
Neeraj Beeknoo1* and Rodney P. Jones2
1King's College Hospital, London, UK.
2Healthcare Analysis and Forecasting, Worcester, UK.
(1) Rui Yu, Environmental Sciences & Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, USA.
(1) Molobe Ikenna Daniel, International Institute of Risk and Safety Management (IIRSM), Nigeria.
(2) Suman Hazarika, International Hospital, Radiology Dept., Guwahati, India.
(3) H. S. Anwith, Kempegowda Institute of Medical Sciences, Bangalore, India.
(4) Imran Aslan, Bingöl University, Turkey.
Complete Peer review History: http://www.sciencedomain.org/review-history/18169
Aims: To explore the interplay between information asymmetry and financial forecasting in the context of the English National Health Service (NHS).
Study Design: Synthesis of the political context of the English NHS and how this impacts on the information flows to assist NHS accountants with financial forecasting.
Place and Duration of Study: Monthly hospital activity data across the whole of England between 2006/07 and 2016/17, and monthly deaths by place of residence within England between 2000 and 2016.
Methodology: Running 12-month totals. Cumulative activity within a financial year (April to March) adjusted for available working days per month, with examples calculated for the 2016/17 financial year.
Results: The NHS in England operates in a tightly constrained policy-directed manner, in which government health agencies may omit to communicate essential information that is contrary to current policy. Long-term trends show anomalous behavior which has been used to ‘blame’ the NHS for failure to constrain health care demand, and as the basis of the need for further ‘corrective’ policies. A running 12-month total is presented as a useful method to detect activity trends subject to non-standard behavior. The role of volatility associated with health care trends is emphasized. Volatility arises from Poisson-based variation due to size, and the effects of the environment on human health, i.e. changes in weather, air quality, noise levels, and outbreaks of infectious agents. Volatility is therefore location specific. After adjusting for the number of available work days per month, cumulative monthly activity data can also be used to calculate year-end outturn from any point in the financial year. Multiple years of historic data can be converted into the current financial year equivalent which enables both the median, minimum and maximum potential outturn to be estimated, i.e. with allowance for location-specific volatility. For example, activity totals for month six of the 2016/17 financial year for emergency admissions across England can be multiplied by anywhere between 2.001 to 2.046 to estimate year end, while costs related to end-of-life can be multiplied by 1.945 to 2.258 in West Berkshire or between 1.890 to 2.304 in Redditch (as examples of the effects of size and location).
Conclusion: Information asymmetry is not unique to the NHS in England. Running 12-month totals are a simple method which can be employed by finance departments to detect unusual long-term trends. The activity multiplier method, based on multiple years of historic data, is simple and can be used to estimate a likely year-end position, along with upper and lower limits.
England; NHS; financial; forecasting; year-end; information asymmetry; policy-based evidence; end-of-life; costs.
DOI : 10.9734/BJMMR/2017/31474Review History Comments