Literature DB >> 27310474

How Healthcare Can Refocus on Its Super-Customers (Patients, n =1) and Customers (Doctors and Nurses) by Leveraging Lessons from Amazon, Uber, and Watson.

Evelyne Kolker1, Vural Özdemir2,3,4,5, Eugene Kolker5,6,7,8,9.   

Abstract

Healthcare is transforming with data-intensive omics technologies and Big Data. The "revolution" has already happened in technology, but the bottlenecks have shifted to the social domain: Who can be empowered by Big Data? Who are the users and customers? In this review and innovation field analysis, we introduce the idea of a "super-customer" versus "customer" and relate both to 21st century healthcare. A "super-customer" in healthcare is the patient, sample size of n = 1, while "customers" are the providers of healthcare (e.g., doctors and nurses). The super-customers have been patients, enabled by unprecedented social practices, such as the ability to track one's physical activities, personal genomics, patient advocacy for greater autonomy, and self-governance, to name but a few. In contrast, the originally intended customers-providers, doctors, and nurses-have relatively lagged behind. With patients as super-customers, there are valuable lessons to be learned from industry examples, such as Amazon and Uber. To offer superior quality service, healthcare organizations have to refocus on the needs, pains, and aspirations of their super-customers by enabling the customers. We propose a strategic solution to this end: the PPT-DAM (People-Process-Technology empowered by Data, Analytics, and Metrics) approach. When applied together with the classic Experiment-Execute-Evaluate iterative methodology, we suggest PPT-DAM is an extremely powerful approach to deliver quality health services to super-customers and customers. As an example, we describe the PPT-DAM implementation by the Benchmarking Improvement Program at the Seattle Children's Hospital. Finally, we forecast that cognitive systems in general and IBM Watson in particular, if properly implemented, can bring transformative and sustainable capabilities in healthcare far beyond the current ones.

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Mesh:

Year:  2016        PMID: 27310474     DOI: 10.1089/omi.2016.0077

Source DB:  PubMed          Journal:  OMICS        ISSN: 1536-2310


  5 in total

1.  AI Techniques for COVID-19.

Authors:  Adedoyin Ahmed Hussain; Ouns Bouachir; Fadi Al-Turjman; Moayad Aloqaily
Journal:  IEEE Access       Date:  2020-07-08       Impact factor: 3.367

2.  A Systematic Review on the Use of AI and ML for Fighting the COVID-19 Pandemic.

Authors:  Muhammad Nazrul Islam; Toki Tahmid Inan; Suzzana Rafi; Syeda Sabrina Akter; Iqbal H Sarker; A K M Najmul Islam
Journal:  IEEE Trans Artif Intell       Date:  2021-03-01

Review 3.  Artificial intelligence in healthcare: past, present and future.

Authors:  Fei Jiang; Yong Jiang; Hui Zhi; Yi Dong; Hao Li; Sufeng Ma; Yilong Wang; Qiang Dong; Haipeng Shen; Yongjun Wang
Journal:  Stroke Vasc Neurol       Date:  2017-06-21

4.  Can nurses remain relevant in a technologically advanced future?

Authors:  Joseph Andrew Pepito; Rozzano Locsin
Journal:  Int J Nurs Sci       Date:  2018-10-04

5.  How to catch trends using MeSH terms analysis?

Authors:  Ekaterina V Ilgisonis; Mikhail A Pyatnitskiy; Svetlana N Tarbeeva; Artem A Aldushin; Elena A Ponomarenko
Journal:  Scientometrics       Date:  2022-02-21       Impact factor: 3.801

  5 in total

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