The algorithmic approach to guidelines has been introduced and promoted on a large scale since the 1970s. This flowchart representation of step-by-step clinical logic guides the management of a patient with symptoms, clinical signs, or results of technical examinations. The transition from one step to the next is mostly dichotomous, which means that only one out of two choices can be made at each step. Moreover, the logic is serial: only one pathway can be followed by a single patient.
The original purpose of algorithmic guideline implementation was twofold. First, with continuing concern over the rising costs of health care, health policy makers have been impressed by the high levels of variation and inappropriateness in medical care. Second, in resource-poor settings, nurses provide frequently the first-line medical care. To deal with the relative lack of knowledge in this medical professional group, algorithms have been used to help rationalise and standardise clinical decision making, an approach considered so far to be effective and efficient [1
]. Algorithms have been pivotal in the task-shifting or task-sharing by nurses that has been promoted over the last 10 years to increase access to antiretroviral therapy.
Clinical algorithms are often controversial: on the one hand, they have often been considered with suspicion because of their restrictive, "cookbook"-medicine approach. On the other hand, they have been viewed as a valuable educational tool, providing neophytes with a method for making diagnostic and therapeutic decisions in clinical practice apprenticeship [2
]. Although both of these views may make sense, none of them has been validated.
Algorithms usually lead to a single diagnosis, potentially neglecting another serious one, leading to delayed investigations and treatment. In some situations, the burden of harm might grow considerably: when we follow an algorithmic guideline step-by-step, it is obvious to stop at the first diagnosis corresponding with the patient's findings. It induces a tunnel-vision and limits the search of certainty in diagnosis.
Epidemiology shows the relevance of the dynamics of the disease spectrum in time as well as in space. The utility of a given clinical algorithm can vary substantially across different epidemiologic and socioeconomic contexts. As a result, every algorithmic guideline must be adapted to every local context and regularly revised [3
]. Moreover, the emphasis on development and deployment of medical guidelines has led to the introduction of multiple versions addressing the same problem: uncertainty is increased with the fact that clinicians have to select among several guidelines. Barak et al. tried to solve this problem by creating a method of text-to-algorithm conversion [2
Several studies have evaluated or compared guidelines but none of them has compared them in terms of complexity or harm [4
]. Wabitsch, in his efforts to achieve reproducible, clinically flexible and applicable guidelines, developed a systematic procedure to tailor guidelines to specific settings. Doing this, he compared two modified guidelines with a global one, but he did not consider harm [3
]. Because of the impact guidelines may have on clinical practice, Pearson et al. proposed a method to develop, use and compare them [8
]. This methodology-the Clinical Algorithm Nosology (CAN)-measures overall design complexity independent of algorithm content, quantitatively delineates algorithm differences, and qualitatively estimates the potential impact of such differences on patient care, by scoring the degree of similarity. This simple method may help clinicians to make the best choice between several algorithms about the same subject or complaint in their setting.
At the end of 2007 the global estimate of the number of people living with HIV was about 33,2 million, with more than 96% in low- and middle-income countries [9
]. In Rwanda, HIV prevalence turns around 2,9 to 3,2% [10
].Except for some countries, the majority of people living with HIV still have no access to highly active antiretroviral therapy (HAART). Chronic cough is a key problem of medical decision making in the management of immune-compromised HIV patients, since a large cluster of dangerous but treatable diseases present this way.
The purpose of this research was to compare virtually three algorithmic guidelines intended for use at a tertiary level in terms of sensitivity, delay to diagnosis, harm, complexity and similarity. We intended to evaluate how complexity relates to accuracy and expected harm. We hypothesized that increasing complexity, thereby approaching the clinician's intuitive logic, improves accuracy and diminishes expected harm, but that, on the other hand, the degree of complexity can become too high and make the algorithm less useful in practice.