How Dr Lal Path Labs Enhanced Customer Satisfaction

Dr Lal Path Labs (LPL) is one of India’s top diagnostic chains that provides diagnostic healthcare services. With over 3,368 diagnostic and related healthcare tests and services offered, the company is capable of performing substantially all of the diagnostic healthcare tests and services currently prescribed by physicians in India. The challenge The diagnostic service provider wanted to steer clear of the prevalent manual process of getting customer and test data including patient demographics & clinical details -- test ordered, refereeing doctor or any other special instruction. The transcribing errors were a pain for the lab operations as these resulted in hassle to the patient in the form of either wrong demographic details or wrong tests performance on the sample given. Lack of proper infrastructure especially Internet service provider in tier II and tier III was a serious challenge. Since the business is widely spread across the country, the solution needs to be accessed from tier II & tier III cities. However, lack of reliable Internet providers in those cities was creating a severe bottleneck for them, says the CIO of LPL, Munender Soperna. The project implemented They launched a project called e- Registration to do away with the challenges. 'e-Registration' is a .net application hosted on Microsoft Azure cloud and integrated with LPL’s private cloud with the help of an Electronic Service Bus (ESB) in between. An automatic sync is enabled for the auto transfer of data from Azure cloud to the ESB layer which in turn communicates with private cloud of LPL which hosts the ERP of LPL. As a healthcare diagnostic chain, LPL tries to deliver quality healthcare and ensure availability for patients in urban areas as well as remote geographical locations. In order to balance cost and drive capacity utilization of super specialty testing equipment, the company has set up labs in different regions of the country. To enable patient care, a network of trained phlebotomist with sample collection infrastructure has been developed resulting in a Hub and Spoke model for enabling patient care, driving capacity utilization and optimizing cost for Super Specialty Testing Labs (SSTL). Practically even after opening so many labs the major share of the business was being handled manually. The business of walk-ins was already on the automated path, with facilities well equipped in terms of infrastructure, whereas the network business which constitutes 70% of the total business was being handled manually. The said solution came handy to bring in automation and increase efficiency. Out of the annual load of 10 million patients/year and 30 million samples/year, the automation in the form of “e-Registration” ensured ease of access resulting in overall improvement in customer satisfaction and reduction in turnaround time of reporting for the manual business of network till time. The cost involved for development and implementation of e-Registration is Rs 21 lacs. The impact With all the patient data capturing taking place at the source, there is a significant improvement in the Turn Around Time (TAT) per sample. As soon as the sample reaches the processing labs, it is directly sent to the concerned department for analysis / testing & it is not held at the sample distribution area for it to be registered / transcribed in the system. This resulted in reduced turnaround time of 1.5 hours per sample for more than 10000 samples per day. It also helped reduce cost on retesting. The data captured at source has brought in the reduction in the transcription errors as opposed to earlier system where the manually filled test requisition form used to arrive at the processing locations along with the samples and the backend data entry team used to do a transcription job to enter the data in the ERP which would lead to a chance of data entry errors. Saving resulted from the reagent cost and raw material cost for the 0.1 % cases of wrong test performed because of the transcription errors. Plus, there was the saving on the license cost. The chances for revenue leakage are eradicated with the implementation of this solution as the data is captured at the source and the respective amount also gets stored in the system. The same is visible throughout the lifecycle chain of that sample. All this assured 100 % revenue assurance as the entire data is captured electronically. Impact created on stakeholders
  • Reduction of Turn Around Time by 1.5 hours for more than 10000 samples/day
  • Reduction in manpower cost as the number of data entry operators were moved to other clerical works
  • Due to reduction in transcription errors, customer satisfaction has reached to a substantially higher level than that of previous times
  • Availability of system generated lab forms & cash receipts ensures customer satisfaction at the source location of sample collection i.e. Franchisee location
  • Reduction in financial losses as there are less number of cases of test corrections
Following are the techniques that were followed by the organization to encourage the acceptability amongst the users. Inclusion of all key stakeholders in the requirement gathering phase was ensured so that all the requirements from the users end are incorporated and the user feels attached to the implementation at all points. There were multiple rounds of performance testing and optimization of the application before going live. The future plans The project is envisaged in such way that it could cater to entire business volumes which are generated from the network of franchisees and pick up points (60 % of the total business volumes) which constitute a sum of 25,000 patients/day. Out of the entire network business, presently the solution is catering to 13,000–15,000 patients per day.  Additionally, the business volumes are expected to grow at a sustained rate year on year. The solution would remain optimized and scalable to cope up with the increasing volumes. This was one of the main reasons for hosting it on Azure platform apart from the manageability and uptime of the application on the cloud.

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