We have designed a novel metric that allows us to measure qualitative sources of information (changing it to a quantitative information) and offer the user the ability to integrate qualitative information alongside quantitative with a high degree of accuracy. This improves decision making significantly. The current challenge with conventional databases, like Google for example, is that they offer a biased view of the contained information. They anchor around a “PageRank” algorithm that is designed to show you one perspective and prioritize the data. These databases search algorithms are designed only in respect to the raw data on each page (like keywords), being the Quantitative Data. Yes, this pagerank algorithm is always changing, but still “controls” the information that is available to you. The Qualitative Data that we add to algorithm would include important sources of information that are not typically captured on screen. For example, how trustworthy is the source of the information? What is the reputation of the person that commented or posted. What is the intuitive interpretation of the information? The essence of the software platform is a transferable vote. Members are allowed to post questions and are asked to answer the questions or transfer the vote to members they deem able to answer them. In this way, specialties, trust, and all quantitative value is derived and added to the PageRank as a scaling factor. So then the algorithm becomes PageRank + Qualitative Information= Prioritized Results
The user can then, with a homepage slider, adjust the algorithm so that the qualitative/quantiative balance is changed. This would change the information structure that is returned and offer the user multiple perspectives of the source information.
Our proposed SaaS platform will be hosted on a secure Azure server that will offer a bridge to powerful microsoft applications. The platform will also have a “health map” that can associate database information geographically. The individuals that can add data to the data base will be approved medical professionals only. The information posted will be synchronized with the medical professionals weighted vote and trust of their colleagues (like a peer review). This is a important step in creating a high quality information source and database. A transferred vote can be considered a type of trust or “unit” of social capital (similar to a peer review). In the medical field, referral to a specialist may be understood as a type of trust with an associated value. As an example, if I were to ask a senior hospital staff member for a reference to a specialist in the area, how valuable would it be compared to my GP’s reference? Similarly, this social capital can be aligned with knowledge and peer reviewed papers that the senior staff members likes vs the ones that my GP likes. In this way, qualitative and quantitative information/value can be separated or combined offering the individual or group decision making of greater value along with information of higher quality.
Once data is gathered, it must analysed. The objective of data interpretation and subsequent analytics is twofold. The first objective is to draw an accurate and comprehensive picture of the health conditions in a targeted area. This picture can be drawn to a level that serves the general population of travellers or it can be further refined to serve a more specialised section of the population such as military, business investment, major NGO’s. The second objective is to spot small changes before they become tipping points to trends or epidemics. As Malcolm Gladwell wrote in his book, The Tipping Point; “Epidemics are sensitive to the conditions and circumstances of the times and places in which they occur”. Analytics serves to help understand the relationship between health conditions and circumstances of the time and place.
Relationships between cause and effect are patterns that can be retained and incorporated into a library. A complex algorithm, using this library of patterns will further automate the analytical process. As more patterns are recognised or existing ones defined, they are added to the library. A library of patterns, combined with an algorithm can provide a more comprehensive picture of health conditions. It also represents a powerful predictive tool on the contagion and and travelling effect of pathogenic diseases.
In 2012, the healthcare consumed 10.9% of Canada’s gross domestic product [1] whereas United States spend a whopping 17.6% on healthcare expenses [14]. With a large portion of our country’s GDP being invested for healthcare, it is important to improve our understanding of ecology and complex health issues as this equates to not only bettering people’s lives but also reducing excess burden on the National economy. Finding integrated solutions to health issues requires us to focus on both the environment and health parameters. Geographic analysis allows users to explore and overlay data by location and to generate clear and accessible maps and data reports that are powerful tools for project development, community outreach, and policy design [16]. Geographic information systems (GIS) holds great promise in the healthcare sector by allowing us to gain insight into the relationships between environmental exposure and illnesses [2]. GIS are computer-based systems that can dynamically link the location and attributes of things into an analytical environment in order to build insights [3]. The opportunities to apply GIS in healthcare are myriad, specifically in areas such as tracking disease progression, identifying contributing factors to the spread of illnesses, and locating pockets of abnormally high health risk indicators [4]. This leads to faster, better and more robust understanding and decision making-capabilities in the public health sector.
The scope of GIS in healthcare is definitely not limited to the public health sector, but it can also prove to be valuable to the government, pharmaceutical and travel industries. Despite the large numbers of residents from United States and Canada who travel internationally every year, research has shown that their overall risk awareness and practice concerning preventive travel health measures, especially the use of itinerary-specific immunizations, was low. [21]. Hence a geospatial map showing the risks associated with travel to specific regions is very much necessary. The data, if presented in a user friendly fashion, can help raise awareness and increase rates of preventative health practices before travel. This in turn can help the government save a significant amount of money. To overcome these issues, more relevant information is required [4]. GIS and other geospatial technologies offer tremendous benefits for the healthcare industry and they offer a unique way of combining and analyzing information. GIS plays a critical role in determining where and when to intervene, improving the quality of care, increasing accessibility of service, finding more cost-effective delivery modes, and preserving patient confidentiality while satisfying the needs of the research community for data accessibility. With the possibilities of GIS in healthcare being myriad, it has become increasingly important for accurate and nuanced analysis and decision support.
