Bottlenecks and reflections from turnkey installation of MRI in a tertiary care Greenfield project: a case study.

BACKGROUND: Turnkey projects are often pegged to be the solution for coordination issues and are common in procurement and installation of high-end expensive equipment. Considering the scale, cost and complexity of high-end diagnostic services like MRI, challenges during installation and commissioning have been commonly seen ever since the early days. The current case study elaborates on the lessons learnt from on-ground issues pertaining to delays in MRI installation in a Greenfield project. METHODS: Root cause analysis with Ishikawa chart was done. RESULTS: On detailed root cause analysis of the 5 broad issues, 20 causes for project delay were identified. These fall into three broad themes that can potentially affect performance of leadership. CONCLUSION: There are three key lessons/takeaways from the current case study. First, establishing proactive feedback loops and communication between all stakeholders. Second, the leadership should have strong control on events and milestones of the project by leveraging project management techniques and technologies. Third, unity of command and unity of direction are of paramount importance to steer the project out of doldrums. These lessons can be useful for healthcare leaders in effective project management.

It's the data, stupid: Inflection point for Artificial Intelligence in Indian healthcare.

Indian healthcare is fast growing and with significant chunk of it being in small, fragmented, informal sector; Artificial Intelligence (AI) is pegged as a magical tool for a better healthcare system. There is an inclination to merely mimic the US approach in the on-going policy making and legislative exercises, which can have serious fallouts for Indian healthcare. India needs a different approach to suite her unique requirements. In this regard, each of the five stages in AI development lifecycle has been analyzed in the light of current on-ground realities. These boil down to three fold challenges of how to increase adoption of digital health, prevent data silos and create maximum value from data. Availability of quality data for value addition without barriers and restrictions is the common denominator for leveraging the full potential of AI. This requires liberal policies enabling secondary use of data in developing countries with rapidly growing healthcare sector akin to India. This has to be carefully balanced with data privacy and security. Restrictive healthcare data policies and laws can slow down adoption of digitization, perpetuate status-quo, be biased towards the incumbent players, cause Industry stagnation and thus will do more harm than good. It is therefore the data policies that will make or break AI in Indian healthcare.

The Holy grail of healthcare analytics: what it takes to get there?

BACKGROUND: Indian healthcare is rapidly growing and needs efficiency more than ever, which can be achieved by leveraging healthcare analytics. National Digital Health Mission has set the stage for digital health and getting the right direction from the very beginning is important. The current study was, therefore, undertaken to find what it takes for an apex tertiary care teaching hospital to leverage healthcare analytics. AIM: To study the existing Hospital Information System (HIS) at AIIMS, New Delhi and assess the preparedness to leverage healthcare analytics. METHODOLOGY: A three-pronged approach was used. First, concurrent review and detailed mapping of all running applications was done based on nine parameters by a multidisciplinary team of experts. Second, capability of the current HIS to measure specific management related KPIs was evaluated. Third, user perspective was obtained from 750 participants from all cadres of healthcare workers, using a validated questionnaire based on Delone and McLean model. RESULTS: Interoperability issues between applications running within the same institute, impaired informational continuity with limited device interface and automation were found on concurrent review. HIS was capturing data to measure only 9 out of 33 management KPIs. User perspective on information quality was very poor which was found to be due to poor system quality of HIS, though some functions were reportedly well supported by the HIS. CONCLUSION: It is important for hospitals to first evaluate and strengthen their data generation systems/HIS. The three-pronged approach used in this study provides a template for other hospitals.