Reflection on the Learning Launch tool for Design Thinking 

 Challenge 

 Emergency Departments in general are usually very busy sections within hospitals and in South 

 Africa especially , both in the private sector and the public sector , these departments are plagued by 

 process flow disruptions and interruptions that can potentially impact clinical care . This has been 

 potentiated by the CoVID-19 pandemic . One of the main problems that create bottlenecks in patient 

 flow is data exchange between key health care workers in the patient care pathway . All systems of 

 care , especially in the private sector have information technology systems but unfortunately , these 

 systems do not communicate with each other . 

 My company is a healthcare technology start - up that is developing a mobile health ( mHealth ) 

 technology platform that is patient driven and which brings all of the systems together with the 

 patient - side application being the hub for all the other systems to communicate . We have 

 conceptualized a Minimum Viable Product ( MVP ) thus far and are taking this component to the 

 design and implementation stage but were wondering if this would indeed provide an increased 

 value for the patient in their pathway of care . 

 My team and I hypothesized that our platform would indeed create value , not only for the 

 patient , but for the other healthcare workers in the service delivery process . We held several key 

 assumptions that supported this hypothesis . The system would be scalable if it was effective and 

 efficient as an integrative technology tool to link data systems together as it had the following 

 capabilities : an ability to handle big data using secure and dependable cloud storage services , 

 algorithmic systems that utilized a narrow artificial intelligence approach to information processing , 

 and a connectivity that relied on the Internet of Things ( IoT ) to link all other systems together 

 through this central patient - driven platform . 

 The second assumption was that it was executable , being built on cross - platform mobile models 

 that had minimal reliance on high - ended technology , with most of the back - end data processing 

 handled by cloud based servers . The third assumption was that it was dependable , again because it 

 was base on low - code approaches to platform development , and reliant on proprietary cloud based 

 storage solutions that offered as close to 95.5 % to 99.9 % up - time , depending on the package of 

 services purchased by our company . Lastly , it assumed that these fundamental , yet rigorous and 

 comprehensive features , offered an extensive value offering for the main clients , who were the 

 patients . 

 We elected to test this hypothesis of client acceptance using a small experimental approach , 

 prioritizing execution and dependability as our key deliverables for the MVP . 

 Selection 

 We considered that the Learning Launch toolkit in the Design Thinking arsenal would be our 

 best approach to trialing our MVP platform with a test group of patients in a real Emergency 

 Department scenario . As the MVP was already past conceptualization , tools such as visualization 

 story - telling and mind - mapping would not offer us the best understanding of what our clients felt 

 about our product . We had already do intensive market research prior to conceptualization and had 

 obtained a vast amount of pre - development data about what the client ( patient ) felt were the 

 problems when they presented for emergency care . The central theme in all of this was that there 

 seemed to be long periods of time when they were left alone , unsure of what was going on . 

 Secondary analysis of these time periods through independent time - motion studies conducted by 

 our internal market research team showed that a large amount of time was spent either providing 

 information or retrieving information , which detracted from patient interaction , even if not for 

 direct clinical care and this was noticed by the client - patient who flagged this as a negative on their 

 entire patient experience . 

 Experimentation then would provide us with direct information on whether our platform would 

 streamline the data exchange process and if this would directly improve patient flow management 

 and indirectly improve the client - patient experience . 

 Application 

 We pre - loaded the platform onto several mobile devices and linked them all to the local wifi 

 network at a private emergency department . We then linked them all to our local server which 

 would emulate the cloud system that we would be using . We provided scribes for the clinician so 

 that his primary clinical records would not be neglected so that he could also use our platform to 

 record clinical encounters . We then also linked these devices to the databases of the local hospital . 

 We were unable to link these devices and the platforms to the radiology and laboratory systems , as 

 we were unable to get access from the various IT departments concerned . 

 Patients presenting to the Emergency Department would then have simulated pre - loading their 

 demographic and background information into the platform and the present for a consultation . The 

 clinician would record relevant information into the platform , the system then synching with our 

 local server to update this information . 

 Insight 

 We obtained much information about the acceptance of our platform as well as its utility to 

 improve data handling and information exchange . Our data flow mapping processes had been 

 confirmed by the experimentation conducted and we were optimistic about its further development 

 into a fully - fledged proof of concept ( POC ) version . The system initially met with some resistance 

 from patients who were anxious about personal data handling and confidentiality , but when we 

 briefed them while they were awaiting triage in the Emergency Department , we had a 100 % 

 enrollment for the experiment . The clinician on duty in the single - doctor cover Emergency 

 Department was also keen to try the platform out , as he was fully aware of the delays that a paper- 

 based or unconnected computer based system caused . 

 We believe that the experimental approach that we had chosen was the best from a design 

 thinking methodology perspective but was obviously guided by the stage of the product 

 development that we were at already . We can not believe that another tool would have been 

 beneficial for us so far down this product life cycle . 

 Approach 

 The experimental approach for us was ideal to understand the implementation , utility and 

 barriers to acceptance of our MVP prior to full product design and development . The other tools 

 available for a design thinker such as visualization and mind - mapping may have been extremely 

 beneficial had we know about these before - hand . We would have found them useful when we 

 applied them to our design and development process . However , despite this , we believe that we 

 have analyzed the what is , what if , what wows and what works methodology satisfactorily to 

 develop a product that we are extremely enthusiastic about . 


EMAIL: 
ID_NUM: 
NAME_STUDENT: 
PHONE_NUM: 
STREET_ADDRESS: 
URL_PERSONAL: 
USERNAME: 

### Task: You are a PII (Personal Identity Information) Detection. Given a essays written by students, you have to identify ONLY the following:
NAME_STUDENT - The full or partial name of a student that is not necessarily the author of the essay. This excludes instructors, authors, and other person names.
EMAIL - A student’s email address.
USERNAME - A student's username on any platform.
ID_NUM - A number or sequence of characters that could be used to identify a student, such as a student ID or a social security number.
PHONE_NUM - A phone number associated with a student.
URL_PERSONAL - A URL that might be used to identify a student.
STREET_ADDRESS - A full or partial street address that is associated with the student, such as their home address.
### Input: Reflection on the Learning Launch tool for Design Thinking 

 Challenge 

 Emergency Departments in general are usually very busy sections within hospitals and in South 

 Africa especially , both in the private sector and the public sector , these departments are plagued by 

 process flow disruptions and interruptions that can potentially impact clinical care . This has been 

 potentiated by the CoVID-19 pandemic . One of the main problems that create bottlenecks in patient 

 flow is data exchange between key health care workers in the patient care pathway . All systems of 

 care , especially in the private sector have information technology systems but unfortunately , these 

 systems do not communicate with each other . 

 My company is a healthcare technology start - up that is developing a mobile health ( mHealth ) 

 technology platform that is patient driven and which brings all of the systems together with the 

 patient - side application being the hub for all the other systems to communicate . We have 

 conceptualized a Minimum Viable Product ( MVP ) thus far and are taking this component to the 

 design and implementation stage but were wondering if this would indeed provide an increased 

 value for the patient in their pathway of care . 

 My team and I hypothesized that our platform would indeed create value , not only for the 

 patient , but for the other healthcare workers in the service delivery process . We held several key 

 assumptions that supported this hypothesis . The system would be scalable if it was effective and 

 efficient as an integrative technology tool to link data systems together as it had the following 

 capabilities : an ability to handle big data using secure and dependable cloud storage services , 

 algorithmic systems that utilized a narrow artificial intelligence approach to information processing , 

 and a connectivity that relied on the Internet of Things ( IoT ) to link all other systems together 

 through this central patient - driven platform . 

 The second assumption was that it was executable , being built on cross - platform mobile models 

 that had minimal reliance on high - ended technology , with most of the back - end data processing 

 handled by cloud based servers . The third assumption was that it was dependable , again because it 

 was base on low - code approaches to platform development , and reliant on proprietary cloud based 

 storage solutions that offered as close to 95.5 % to 99.9 % up - time , depending on the package of 

 services purchased by our company . Lastly , it assumed that these fundamental , yet rigorous and 

 comprehensive features , offered an extensive value offering for the main clients , who were the 

 patients . 

 We elected to test this hypothesis of client acceptance using a small experimental approach , 

 prioritizing execution and dependability as our key deliverables for the MVP . 

 Selection 

 We considered that the Learning Launch toolkit in the Design Thinking arsenal would be our 

 best approach to trialing our MVP platform with a test group of patients in a real Emergency 

 Department scenario . As the MVP was already past conceptualization , tools such as visualization 

 story - telling and mind - mapping would not offer us the best understanding of what our clients felt 

 about our product . We had already do intensive market research prior to conceptualization and had 

 obtained a vast amount of pre - development data about what the client ( patient ) felt were the 

 problems when they presented for emergency care . The central theme in all of this was that there 

 seemed to be long periods of time when they were left alone , unsure of what was going on . 

 Secondary analysis of these time periods through independent time - motion studies conducted by 

 our internal market research team showed that a large amount of time was spent either providing 

 information or retrieving information , which detracted from patient interaction , even if not for 

 direct clinical care and this was noticed by the client - patient who flagged this as a negative on their 

 entire patient experience . 

 Experimentation then would provide us with direct information on whether our platform would 

 streamline the data exchange process and if this would directly improve patient flow management 

 and indirectly improve the client - patient experience . 

 Application 

 We pre - loaded the platform onto several mobile devices and linked them all to the local wifi 

 network at a private emergency department . We then linked them all to our local server which 

 would emulate the cloud system that we would be using . We provided scribes for the clinician so 

 that his primary clinical records would not be neglected so that he could also use our platform to 

 record clinical encounters . We then also linked these devices to the databases of the local hospital . 

 We were unable to link these devices and the platforms to the radiology and laboratory systems , as 

 we were unable to get access from the various IT departments concerned . 

 Patients presenting to the Emergency Department would then have simulated pre - loading their 

 demographic and background information into the platform and the present for a consultation . The 

 clinician would record relevant information into the platform , the system then synching with our 

 local server to update this information . 

 Insight 

 We obtained much information about the acceptance of our platform as well as its utility to 

 improve data handling and information exchange . Our data flow mapping processes had been 

 confirmed by the experimentation conducted and we were optimistic about its further development 

 into a fully - fledged proof of concept ( POC ) version . The system initially met with some resistance 

 from patients who were anxious about personal data handling and confidentiality , but when we 

 briefed them while they were awaiting triage in the Emergency Department , we had a 100 % 

 enrollment for the experiment . The clinician on duty in the single - doctor cover Emergency 

 Department was also keen to try the platform out , as he was fully aware of the delays that a paper- 

 based or unconnected computer based system caused . 

 We believe that the experimental approach that we had chosen was the best from a design 

 thinking methodology perspective but was obviously guided by the stage of the product 

 development that we were at already . We can not believe that another tool would have been 

 beneficial for us so far down this product life cycle . 

 Approach 

 The experimental approach for us was ideal to understand the implementation , utility and 

 barriers to acceptance of our MVP prior to full product design and development . The other tools 

 available for a design thinker such as visualization and mind - mapping may have been extremely 

 beneficial had we know about these before - hand . We would have found them useful when we 

 applied them to our design and development process . However , despite this , we believe that we 

 have analyzed the what is , what if , what wows and what works methodology satisfactorily to 

 develop a product that we are extremely enthusiastic about .
### Output: 