The method that chosen and used for collecting and analysing data meant to give form to the human thinking and process of solving problems. The effectiveness in detecting the problems and making decisions over them depends on statistically collected.
The decision-making procedure involves “detecting problems, defining priorities, identifying innovative solutions, and allocating resources for improved health outcomes.” (Cibulskis & Hiawalyer, 2002) These are fundamental realities that have been ignored or little efforts have been placed to enhance them for greater information outcome. The timeliness, accurateness and relevance of information are crucial for decision makers.
This paper focuses on collection of public health data that is required for making managerial decisions at personal, provisional, regional, national and international levels. Some of the vital statistical information required to improve decision-making include the census reports, reports filed after some vital events, health surveillance reports, data from resources tracking procedures, statistics of facilities and other household surveys.
The power of information for strategic change is evident on some historical examples of statistical data collection and analysis that used in the past for better decision-making procedures.
Such examples include “The United Nations’ Standard System of National Accounts” which was discovered years ago by Richard Stone and it helps to define and shape the nation over various views, opportunities on offer and supporting disclosures. The other renowned example is the “Disability-adjusted Life Years (DALY’s)”, which is globally used to shape precedence for investments. (Cibulskis & Hiawalyer, 2002)
Data is a straightforward gauge for characteristics but it has very little or no inherent value or meaning if it is not analyzed, processed or interpreted to allow for generation of patters that create information. Information is what enables one to make improved decisions such as generation of recommendations, creation of rules for actions and change behaviors to support knowledge.
Type of statistical information collected in the health sector
The time, accuracy and the relevance of the information are the requirements for good decisions over services, policies/procedures and behaviours. In the health sector the type of statistical information collected for strategic planning include, “clinical diagnosis and management of illness or injury, quality assurance and quality improvement for health services, detection and control of emerging and endemic disease.”
Others may entail “human resource management, procurement and management of health commodities (including drugs, vaccines, and diagnostics), regulation of toxic exposures, program evaluation, research and other types of policies or programs.” (Walsh and Simonet 1995) This is information that can be very useful for the citizens to make viable claims, choose better health facilities consequently to behaviors and demand the required services and policies that safeguard them.
In the health sector, some of this statistical information is very important for making wise decisions regarding transactional needs. For instance detection and control mechanisms to be installed at the boarder points to control consequences that may be brought about by wide spreading epidemics or infections such as the recent “H1N1” flu that has his various countries all over the world or the “Severe Acute Respiratory Syndrome (SARS)”.
The health sector lacks these urgently required responsive and apparent tracking systems and therefore there is need for performance measures to allow various countries to define priorities. This is an ever-increasing burden forcing the need for provision of evidence as a basis for decision making which leads to more risks. Countries ought to be empowered into measuring key indicators and producing unique evident strategic plans for better information and better health. (Perrin, Kalsbeek, and Scanlan 2004)
Advantages of accurate interpretation of data to improve decision making
With an organized process and procedure of interpreting medical data, it is possible to assemble and communicate the required information in an understandable and timely manner.
A standardized system of collecting, analysing and availing heath recorded data enhances the economies of scale over training, managing software, processes and hardware that is involved.
Considering regular health information as a cumulative high-quality need due to its required spread, produces cumulative augments in the assessment of public goods, thus strengthening credibility and importance of Information interpretation and exchange. The reliability and compatibility of information is furthermore improved through standardizing local as well as multinational systems. (Cibulskis and Hiawalye, 2002)
Conclusion
Today there is no single data collection mechanism, considered as the most adequate over the needs for decision-making procedures. Globally, especially in the health sector, the required technology should be in position of integrating information to produce the necessary indicators or track development towards common goals.
This may call for pre-defined methods for management and distribution of information and definition of iterative systems for collecting core data such as “census, surveys, services statistics, resources and monitoring of vital events.” (AbonZahr and Boerma 2005)
According to The World Health Organization (WHO) as reported by Murry etal, (2003) the key statistical information or data that ought to be collected for improved decision making procedures include census reports, family units inspection reports, community physical condition surveillance reports, reports written after monitoring of important events, statistics involving the health departments and resource follow-up reports.
Reference
AbonZahr, C., and Boerma, T. (2005) “Health Information Systems: The Foundations of Public Health.” Bulletin of the World Health Organization 83 (8): 578–83.
Cibulskis, R. E., and Hiawalyer, G. (2002) “Information Systems for Health Sector Monitoring in Papua New Guinea.” Bulletin of the World Health Organization 80 (9): 752–58.
Murray, C. et al. (2003). “Validity of Reported Vaccination Coverage in 45 Countries.” Lancet 362 (9389): 1022–27.
Perrin, E. B., Kalsbeek, W.D. and Scanlan, T. M. (2004) Toward a Health Statistics System for the 21st Century. Summary of a workshop, Division of Behavioral and Social Sciences and Education, National Research Council and Committee on National Statistics, National Academy of Sciences. Washington, DC: National Academy Press.
Walsh, J. A., and Simonet, M. (1995) “Data and Data Needs for Health Sector Reform. “Health Policy 32 (1–3): 295–306.