Open Healthcare is a startup that focuses on centralization of healthcare data in a digital platform, we are aiming to do for healthcare what ATMs did for banking.
Currently the system is fragmented and everyone works in silos, which makes it difficult to understand patient records and reconcile patient history. Data capturing and centralisation must be taken more seriously in order to generate usable data that can aid interventions for tackling fraudulent claims, for example, and enabling early detection and containment of communicable diseases.
We must be able to pinpoint where and why funds are spent in the different healthcare areas so that we have an understanding of the population’s health needs and required interventions. Also, if such data is captured across the southern African region, we can determine what diseases patients have had when they come into the country and we go outside the country, and can adequately pick up communicable cases across countries.
Improvements in healthcare should not be perceived as being limited to the first world. Decisions must be taken by African governments to do things better and ensure that the healthcare system is not fragmented.
Data will enable efficiencies in healthcare
Public healthcare systems across the region are buckling under pressure as they are continuing to service more people than originally envisaged. The resulting inefficiency is coupled with a myriad of other challenges such as skills shortages, fragmented systems and unconsolidated data that could have provided a clear picture of the health needs of the region’s population.
The Future Health Index, commissioned by Dutch tech company Philips, paints a bleak picture of our public healthcare system. According to the report, South Africa was ranked last among 19 nations in a global survey that measured healthcare system efficiency – the ability to deliver maximum results at the lowest possible cost. Among some of the biggest bottlenecks in the progression of healthcare in the country is data fragmentation and a lack of data to inform spend vs. health citizen needs.
The authors of the report attributed South Africa’s poor performance to average healthcare spend as a percentage of gross domestic product (GDP) that delivered below average health outcomes. South Africa spends 8.8% of its GDP on healthcare, just over R4 trillion a year, which places it in the mid-field amongst the 19 countries. The US spent 19.1% of its GDP on health and France 11.5%. The lowest spender was the UAE with 3.8%. There is a compelling case for improved budgetary allocation for health, and only usable data can adequately support this.
The adoption of smart applications and innovative technology will go a long way in mending the state of the public healthcare system and facilitating quality, equitable and affordable healthcare services to all.
Data aggregation for population health
This is significant, because the demand for electronic health information exchanges from one healthcare professional to another parallels nationwide efforts to improve the quality, safety and efficiency of healthcare delivery. In a nutshell, HIEs access and properly channel the accumulated data stored in EHR databases, at disparate care locations, where they would otherwise provide little to no value to the health system at large.
However, data aggregation to drive quality improvements should not be restricted to population health outcomes alone (although, this is a high-priority objective). At a more granular level, beneath the system-wide umbrella, individual healthcare practices can benefit greatly from the vast amount of data they produce. By aggregating and analyzing these data, institutions can gain deeper insight of not only clinical, but organizational performance measures, and devise more effective strategies to increase quality and control costs.
Predictive analytics may only be the second of three steps along the journey to analytics maturity, but it actually represents a huge leap forward for many organizations.
Instead of simply presenting information about past events to a user, predictive analytics estimate the likelihood of a future outcome based on patterns in the historical data.
This allows clinicians, financial experts, and administrative staff to receive alerts about potential events before they happen, and therefore make more informed choices about how to proceed with a decision.
The importance of being one step ahead of events is most clearly seen in the realms of intensive care, surgery, or emergency care, where a patient’s life might depend on a quick reaction time and a finely-tuned sense of when something is going wrong.
But high-value use cases for predictive analytics exist throughout the healthcare ecosystem, and may not always involve real-time alerts that require a team to immediately spring into action.
Evolving trends in healthcare have given rise to more actively engaged and informed patients. In this new age, patients are increasingly seen as consumers who are commanding control of how they receive and pay for medical care. As a result, there is a greater push and demand for transparency around costs, quality of care and information-sharing.
With the availability of aggregate data, medical practices position themselves to adequately meet the growing needs of their empowered patients. This accessibility to updated, relevant patient information strengthens patient engagement and satisfaction. For instance, advanced knowledge of what procedures the patient has undergone or what actions have previously been taken at multiple points within the practice, can prevent the frustration, inefficiency and potential for error that arises when these activities have to be duplicated.
Patient satisfaction can also be enhanced when identification lists are consolidated. Properly identifying patients through demographic data can improve satisfaction by avoiding the continuous need to update personal information—a typical occurrence when records are kept in multiple locations. As an aside, the ability to quickly identify new and returning patients, by demographics, across all divisions in an institution could also prevent potential issues should an insurance audit occur.
In addition, data aggregation at a medical facility facilitates the identification of prevalent health problems in the patient pool. This is particularly relevant in terms of managing patient experiences and outcomes, because addressing the most common health issues seen within the practice will undoubtedly reflect on clinical quality reports. Consequently, the accurate evaluation of clinical quality and patient outcomes ensures the practice is trending in the right direction clinically; thereby guaranteeing that compliance is achieved and government incentives are secured.
Likewise, for providers, documented improvements in clinical quality reports could spell opportunities for incentive compensation. Using aggregated data to demonstrate quality benchmark achievements, a bonus structure that mimics industry pay for performance models can be established. These incentive programs are reliant on dependable data analytics that will enable providers to compare data on their performance relative to their peers’ and serve as a strong motivator in meeting outlined performance objectives in turn, this will continuously drive practice improvements.
Along with these advantages, the opportunity to consolidate appointment records and financial reports to develop a better understanding of booking trends and gain a comprehensive view of charges, expenses and adjustments, with intentions of maximizing revenue and maintaining financial integrity, is presented. Moreover, in relation to payments, a “multi-payer information highway” can be created, which forms an evidentiary basis to facilitate payer negotiations and contract renewals.
In the healthcare industry, data is continuously produced; however, fragmentation, disorganization and inaccessibility are known to diminish the utility and value of this information. At the population health level, data aggregation has been shown to positively impact patient outcomes and aid in cost-containment efforts. For medical practices hoping to achieve these same goals, while tapping into a host of other benefits, this tactic is not only recommended, but equally advantageous.
With advanced data aggregation and warehousing capabilities, practices can consolidate medical records from diverse systems to centralize integrations, normalize data and identify trends using a dynamic platform such as Open Healthcare. In this way, patient, clinical and financial data is more meaningful and actionable and can then be used to draw the abovementioned benefits. Additionally, this intuitive system can be easily integrated and customized to generate queries specific to a practice’s needs and requirements.
These functionalities demonstrate Open Healthcare’s commitment to helping medical practices yield the most value from the data they produce in order to transform strategic planning, drive operational success and improve quality of care.
We need the money to complete the platform paying salaries for developers and day to day operations.