A picture is worth a thousand words: translating surgical data into user-friendly information, by Dr Mengyang Zhang

Due to a wider application of new technology in the health sector, the scale and the type of health data being collected are growing. Health information management systems are essential to inform policies and interventions to enhance the health and wellbeing of the population, and to support the long-term sustainability of national healthcare systems. However, in less developed countries it can be a challenge to manage data and extract key messages for policy makers in an efficient way.

In the SURG-Africa project, multiple sources of data are collected on different aspects of surgical service delivery in the three partner countries: Malawi, Tanzania and Zambia. The data we collect range from volumes and types of surgeries, patient outcomes, self-confidence of surgical teams to patient level studies and qualitative evidence exploring deeper behind the numbers. These datasets provide an overview of the current situation, which is helpful to the authorities in the decision-making process, such as the allocation of health resources, the strategic plan of pilot intervention, and in priority-setting for the next phase of work. For example, our data from Tanzania were used to help develop the National Surgical Obstetric and Anaesthesia Plan for the country. There are also other ways in which data can be used. After the hospitals’ data were collected and cleaned, a regional map can be made using these data to show the hospitals’ performance in terms of volume of surgical procedures undertaken. ​

Figure: Average surgical volume of all major procedures by region and country.

As shown in the figure, the map allows to compare performance by region, thereby providing essential information to support the design of strategies for different areas. This technique was used by the World Health Organisation in health equity assessment1. In addition, it can also compare the performance by country. By comparing the average surgical volumes of different procedures, we can see the difference in the types of surgical procedures and the total number of inpatient and outpatient visits during a period of time in different countries. After controlling for patient information (such as socioeconomic factors, demographic characteristics and diagnostic information), relevant results can provide further evidence about the determinants of certain diseases and potential risk factors in certain types of surgery. If these data can be collected for a long time, it will be possible to monitor the standardisation of surgical procedures, the prevention of epidemics and the improvement of clinical pathway management.

 Table: Comparison of hospital efficiency score by indicator and hospital.

At the local level, the health data help district hospitals monitor their performance and their goals. For instance, this table compares the relative ranking between indicators and hospitals displayed in colour codes. The darker green indicates that a hospital’s performance is better than the average level (green), while the light green indicates that the hospital’s performance is below the average level. Then each row shows the rank of a hospital in each dimension. For Hospital 1, the scores in the Indicator 2 and 4 are better than the average. However, the scores of Indicator 6 to 8 are low, which may be the goal of the next round. In addition, the vertical comparison presents the selection of priorities. As for the Indicator 2, the performance of four hospitals is better than the average. Only three hospitals need to strengthen relevant work in this area. These quantitative data may be complemented by the use of qualitative methods in those four hospitals to investigate reasons behind their performance and the experiences gained from the process, which may help to promote the policy nationwide.

For policy makers, data visualisation helps to make better use of information. The table of performance comparison is an example. It is hard to read large amounts of data in a spreadsheet and make comparisons. Data visualisation techniques offer more user-friendly options. As in the example presented here, all information is transformed into an easy to read table. The vertical comparison shows the relative ranking of each hospital in a specific area. This facilitates to identify which hospitals perform well and which hospitals need further improvement. The horizontal comparison shows the performance of a hospital in different domains, highlighting strengths and weaknesses. These data visualization techniques are helpful in supporting monitoring and planning efforts, which provide policy makers and central authorities with an overview of the situation on the ground. As to the SURG-Africa project, the economic analysis of the routine data collected in district hospitals can be used to estimate the intervention impact whether it imposes significant influence on local surgical works.

How did we learn all these lessons, and how did we collect the data? SURG-Africa brought in resources to address data collection challenges in rural areas. And the challenges are severe. The findings by our colleague (Clarke et al. 2021) shows that some hospitals perform well in collecting surgical records, while in other hospitals the share of missing data is over 20%. 
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If countries were prioritising data collection, the information we presented here could be captured by locally funded data collection systems. There is no reason for countries not to start prioritising collecting quality and reliable data, as data help to make the right decisions. But, over all, data helps to build stronger health systems and, consequently, to save lives.    
 

Reference
1. World Health Organization. Urban HEART: urban health equity assessment and response tool. 2010.
2. Clarke M, Pittalis C, Borgstein E, et al Surgical service monitoring and quality control systems at district hospitals in Malawi, Tanzania and Zambia: a mixed-methods study BMJ Quality & Safety Published Online First: 16 March 2021. doi: 10.1136/bmjqs-2020-012751.