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Outlook of Human Element of Data in Life Sciences

Introduction

Today there is hardly any walk of life which is not influenced by the use of data & advanced analytics techniques, be it a choice of food/restaurants, apparel, travel and even medicine is getting transformed by Machine Learning & Artificial intelligence driven platforms. So, what makes Life Sciences & associate industries (Medical, Pharma & Med-Tech) any different. The answer lies in understanding that in Life Sciences, stakeholders deal with human lives (Saving & Improving them). When human lives are at stake, it becomes extremely important to be assured of the highest standard of the safety, security, accountability and ethics in the business practices involved in this industry. As far as data & advanced technologies are concerned, they do have made the decision making of all the stakeholders involved simpler yet powerful, although same quotient of responsibility also goes with it.

There is a rising need of a prospective synergy to integrate the artificial side & the human side of the data. As an example, let’s take a subset of population of a country across all sections/geographies. In that subset there would be groups who would not be availing medical procedures/ certain treatments not since they don’t have the disease or they don’t require them but because, they don’t have the required financial strength. Point being, when an organization/Govt./Other stakeholders use an algorithm (ML/AI based) on such problems, the insights drawn might be flawed if the algorithm is unable to detect/understand such human biases.

To add, certain sections of the society are not represented/ misrepresented more often than not, which increases chances of further bias and thus decision making is not equitable is such cases. Also, there are cases where the patients withhold the information (Plenty of reasons for such a reaction). These scenarios lead to a problem of flawed or incomplete data causing the algorithms to not able to predict correct developments for the future. So, how do we address these challenges for the betterment of all the parties involved; we need to bring and adapt Interoperability between the stakeholders concerned.

For starters, we need to develop a culture of educating people (both at the receiving & providing ends) about data integrity, which can only be achieved by engagement. All the stakeholders need to assess the risk pertaining to regulatory and non-regulatory consequences. Failing to adapt to the required levels of Data Governance can easily lead to gaps in strategic data, flawed algorithms, biased predictions and finally dissatisfied users lacking trust.

 

How is SAP helping their clients counter such challenges related to data?

 

  • Taking an example, in 2018, SAP teamed up with Cerner Corporation in order to build a new electronic health record platform, which would be operating specifically for Non-US based hospitals. As GDPR (General Data Protection Regulation) Act for Europe is tougher in regulation as compared to HIPAA (Health Insurance Portability and Accountability Act), SAP and Cerner are handling data privacy and data security as well as regulatory changes by posting the code in local/regional data centers rather than customers keeping it on-premise. SAP along with Cerner is facilitating the creation of an intelligent hospital (Intelligent Enterprise) by uniting data-driven advances, patient experiences, Treatment Outcomes and enhanced workforce capabilities.

 

  • In a Healthcare environment, largest composition of data belongs to electronic health data of patients & other stakeholders. Typically, this data consists of electronic health records (EHRs), Clinical data from CPOE (Computerized Physician Order Entry), machine generated/sensor data, such as from monitoring vital signs, data generated from prescriptions, insurance related data and other administrative data. SAP Analytics Cloud proves out to be a great help for benefitting and handling such data. It helps in data aggregation through multiple EHRs which could lead to a breakthrough (discovering hidden trends/actionable intel) in the disease treatment procedure, making it a success both financially and clinically.

 

SAP Analytics cloud along with SAP HANA is quite extensively used in the genome study and clinical trials as it helps in figuring out the hidden patterns in such data and how a specific gene would react to a specific drug.

There is a huge potential in the uses of such technologies provided, the human side is given its due consideration.

 

  • Taking another case, Paul HARTMANN AG along with SAP in making a huge difference in people’s lives. They use SAP Data Hub to counter cost pressure in the healthcare practices by improvising and automating processes to reduce costs. SAP Data Hub provided an infrastructure to bring in various data sources together for insights. SAP Data Hub also addressed the pain-point of inventory management, it enabled a Sensor-box which would auto generate requisitions for the products which reach a low level of inventory. It also helped in demand prediction and thus be prepared for any future, in a way they always have the supplies they need to care for patients and to save lives.

 

  • Mercy Health along with SAP is ensuring better patient driven services with standardized analytics techniques for the reorganizing data points, storing them and presenting in an easy consumable format. They have a special focus in the perioperative domain of healthcare analytics for decision making by careful monitoring of key health metrics with the use of SAP. Currently they’re using SAP HANA, SAP Business Objects BI Suite, SAP Predictive Analytics & SAP Lumira for handling their data related needs in order to serve patients and other users better.

 

  • When it comes to data governance/management it is the employees who has to take the ownership to understand the implications associated with it. When it comes to healthcare/Life Sciences, employee management is one of the important factors to consider. Carolinas HealthCare System is taking all the right steps to ensure that right culture of engagement & empowerment is imparted to its employees. SAP SuccessFactors has enabled them to take care of their most important asset, their workforce by understanding the human side of employee oriented data. SAP SuccessFactors ensured that the data was easily available, comprehensible and all into a single platform. It also pushes the workforce to integrate and understand their own future career prospects by continuous self-evaluation. It enables the management to analyse diversity and inclusion of the entire workforce. Most importantly Carolinas HealthCare System along with SAP are trying workforce to take control and judge themselves what is working and what’s not keeping in mind human lives & health are at their mercy.

 

Conclusion

All these cases and examples (and thousands of such success stories) tell us that SAP along with its clients are trying to understand the data in the form that it meant to be understood. They are trying to develop a culture where data governance is not taught but self-learnt along with its human side. When we hear the horror stories of companies getting shutdown to criminal charges, there is enough evidence which suggests that they failed to adapt rapidly to prevent violations and regulatory consequences and risked everything they stood for. And in case of healthcare the cost of such violations can cost people there lives and so much more.

As, Bernd Montag (CEO, Siemens Healthineers) says “By combining the raw computational power of AI with the experience and empathy of human healthcare professionals, we have an unprecedented opportunity to transform the way care is delivered today.”

In order to ensure that the powerful advancements of technologies available to us work positively, it is very important to use it responsibly adhering to global regulations keeping human perspective at the heart of the solutions/offerings.

 In the end it boils down to one simple concept, “whether the stakeholders of the data know, what’s at stake!”

 

References

  1. https://www2.deloitte.com/global/en/pages/life-sciences-and-healthcare/articles/data-integrity-life-sciences.html
  2. https://www.healthcareitnews.com/news/sap-cerner-team-develop-next-gen-ehr-european-hospitals
  3. https://catalyst.nejm.org/patient-led-health-data-paradigm/
  4. https://www.sapanalytics.cloud/resources-healthcare-data-analysis/
  5. https://www.sap.com/india/about/customer-stories/finder.html?tag=industry:healthcare
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