Understanding complexity in low and middle-income countries

One key objective in this chapter is the development of a model for understanding and analysing complexity in public health informatics in LMICs, and the various arguments leading up to this model. The level of formalization of business processes is an example of an important dimension to apply when analysing complexity. In LMICs, the informal sector of the economy will typically be bigger than the formal sector. This informal-formal dichotomy is replicated in many aspects of society and develops as part of the general process of modernization, as the organization of work gets more formalized and rule-based. Computers are ‘dumb’ and only the absolute formalized and structured aspects of work and business processes can be successfully computerized. Limited formalization and standardization means that interaction between the various components of the complex system, that is between business processes and organizational structures in health, will be more informal, less standardized and rule-based, and consequently more ‘complex’ to computerize. The meaning and handling of complexity in LMICs may therefore be different than in more thoroughly modernized countries. We illustrate this aspect of complexity in LMICs with an example from an Asian country, where the computerization of the licensing of health workers faced the problem of a mismatch between regulation

(formal and detailed requirements) and how the regulation was practised (the required categories of courses, for example, did not exist)—which was much more informal than the prescribed procedures.

Hans Rosling raised a similar point at the Global Health summit in Washington DC (June 2015) that the World Health Organization (WHO) strategy of establishing birth and death registration in all countries was doomed to failure in a number of LMICs. This process had taken more than 200 years in Sweden, and many countries would not be ready for implementing this programme due to the state of the processes of modernization and formalization for the various institutions involved. Uncertainty regarding the quality of health data—and more generally of population-based data, such as the lack of reliable census data, is linked to similar aspects of poorly developed institutions, and the heightened complexity in getting reliable estimates.

We first discuss fragmentation of HIS and present a theoretical framework using complexity to understand it, drawing upon concepts of attractors for change drawn from CAS. In discussing strategies for addressing complexity, we take the notion of cultivation from information systems research and illustrate this analysis approach by drawing upon the case of the dashboard in Indonesia. Furthermore, we present a different integration approach based on a data warehouse in Ghana. Both of these cases illustrate aspects of complexity arising from the mismatch between regulation and practice in a LMIC. These different examples help to develop a model for analysing complexity. Finally, we conclude with strategies for developing systems and handling complexity in LMICs.

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