How Open Information and Data Fluidity Can Aid Achieve Global Goals 3
We all know the benefits of open data in healthcare, thus this developing piece will look at the #GlobalGoals3; Health — ‘Ensure healthy lives and promote well-being for all at all ages’
To achieve sub-goal 3.1–4; Opening up and properly allowing stakeholders e.g. the public, health care experts to examine and scrutinize mortality datasets at hospital level (especially that of newborns and children under-fiver) can help identify hospitals with abnormally high mortality rates, and poor clinical practices. This in turn will aid questioning of their health budget and spending data. The end point is to improve accountability on the part of the institutions and avail more option to the patients who can take charge of their healthcare.
Using data to fight diseases, it is said that the amount of resources going into drug discovery for African neglected diseases by Africa in Africa is small. By making this knowledge available in detail, communities, organizations can understand impact, make better decisions on medical products and public health spending can be made on the basis of sound knowledge.
Opening of high-level, niche or expensive drug discovery and disease research data allows for supporting best practices and the best chances of results, people will be able to view positive and negative outcomes, the unproductive duplication of work can be overcome and experts around the world can learn from each other. More so innovators can leverage public health, media, and open data sources to provide real-time information and alerts about disease outbreak based on a number of filters, including location so to allow the public and response organizations make swift decisions.
To achieve sub-goal 3.5; Patterns of drug consumption and associated consequences may vary depending upon geographic and socio-demographic characteristics of a community. Using data to understand the extent and distribution of substance abuse and its adverse consequences within the population (and among specific population groups) is essential in preventive planning. Alcohol and drug problems generally manifest at the community level, it is often best to address them locally.
For effective and efficient planning, it is crucial to identify “hot spots”, i.e., communities that are at greatest risk for substance abuse problems and reasons why the problems persist amongst other catalyzing factors. To do this, the severity of substance abuse among communities can be measured and compared and the community-level consumption and consequence data indentified for individual drug categories.
The selected communities can be ranked by the selected indicators; (number and rate of alcohol-related crashes, arrests for driving under the influence (DUI), arrests for public intoxication, arrests for liquor law violations, and rate of substance abuse treatment episodes with reported alcohol use to mention a few) using a highest-need/highest-contributor model; i.e., community that received a priority score based on their need for intervention (measured by the rate at which an indicator occurred) and their overall contribution to the problem (measured by the frequency with which an indicator occurred.
To achieve sub-goal 3.6, data from government traffic agencies, non-government agencies and medical intuitions must be made available and easily accessible. Both groups must be willing to work together so as to create an environment for data interconnectivity. The public must also be able to access these data.
In achieving this sub-goal, we must look at the following indicators; how many accidents occur in a region or zone, how many people die from the accidents? How many get injured, how many die on the spot and how many die at the hospital, what circumstances lead to the ‘hospital’ deaths after the accident, how long did it take for an ambulance to appear on the scene, how long did it take the ambulance to get to the hospital from accident scene and most importantly for every kilometer of a particular area or zone, how many quick mobile response facilities are there. By asking the above questions and looking at data available per variable, we can begin to connect the dots and fit the pieces of the puzzles together.
This is a developing article.
Blaise Aboh is Data Visualization Architect at Orodata, a civic tech organization translating public data into insightful narratives, leveraging interactive visualizations to uphold transparency, accountability, and civic inclusion.