How Can Big Data Support Learning Process? A ...

How Can Big Data Support Learning Process? A Case Study in Organization Internal Healthcare Provider

Puspita Kencana Sari, Elvira Azis TEBS , Telkom University, Bandung, Indonesia

Abstrack Nowdays Big Data analytics is used in many aspects of organisations. This paper discuss about how big data is used to improve learning process and support learning organization. This paper take case of Yakes Telkom as PT. Telkom internal healthcare provider that has 403 of staff separately in 950 clinics all-over Indonesia. Yakes Telkom has to service more less 150.000 people, including employees, pensions and their dependents. Yakes has online medical record system that integrate all medical data from all clinics. Having huge medical records from its patients, could be used to improve quality of care that they provide. By sharing and analyzing healthcare information, physicians can identify the best treatments for their patients and do service excellent. Comprehensive analytics can also give more correlated view of cost and quality of both in healthcare delivery and administrative process. Furthermore, it could be reduce healthcare expenditure of the organization itself.

Keywords: Big Data; healthcare provider; learning organization

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1. Background. Data is one of important asset had by organization. Data is commonly used to help decision making process in any level of management. Data is also basic for creating knowledge. It then used by organization to improve and innovate process or product. Digitalization almost all data in organization make explotion number of data. Beside increasing the volume, technology development also enable organization to process and analyze variation of data, not only structured data but also unstructured data. Organization should also consider about its velocity and veracity to optimize that large amount of data, either from outside or inside organizitation. Now days, its called as Big Data. Data and information that have pattern can form knowledge. According to Delphi Group research in 2000, 42% knowledge repository in employees‟ brain(Uriarte Jr, 2008). But now in the Big Data era and information technology development, organizations can have knowledge not only from their employees but also from their consumers, partners, and other sources outside them. Big Data could improve the way organizations do businesses even generate a new business. According to research by McAfee and Brynjolfsson from MIT, companies that inject big data and analytics into their operation show productivity rates and profitabilty that are 5% to 6% higher than those of their peers (McAfee, 2012) . Big Data analytics could be used in many field of business, including in healthcare services. Big Data analytics can be used to support five main activites of learning organization defined by Garvin (1993) in Jashpara (2004); systematic problem solving, experimentation with new approaches, learning from their own experience and past history, learning from the experiences and best practices of others, and transferring knowledge quickly and efficiently throughout the organization. This paper propose a concept of Big Data analytics to support learning process in organization especially healthcare service provider. In Indonesia, a big corporate, like PT. Telkom Indonesia.Tbk, commonly has internal healthcare provider which organize healthcare services for all employees, pensions, and their families. With better learning process, it is expected can improve the services and claim-cost. This paper take a case study from Yakes Telkom as internal healthcare provider of Telkom. 2. Theoritical Framework 2.1. Knowledge Management Relation between pieces of data can form information. Information that processed further can become knowledge when one realize and understand the pattern relation among data and information and their implication[4]. Knowledge is one of important asset in an organization. Therefore, organization should optimize knowledge to support the development of product, process, or services. Information technology has been used for supporting knowledge management for decades. Hayes (2011) defined two roles of IT in knowledge 2

management; interactive and integrative application. Integrative applications take the form of structured databases that allow employees to store and retrieve information on past projects, expert finders, electronic bulletin boards through to best practice reports and working papers. Interactive applications take the form of email, desktop conferencing, and discussion forums allowing for interactions with other staff and the garnering of their views and experiences regardless of physical location. Knowledge management and learning organization are two terms that related each other. Smith and Lyles (2011) defined the distinction between learning and knowledge: knowledge being the stuff (or content) that the organization possesses, and learning being the process whereby it acquires this stuff. Therefore, information technology used in knowledge management can also be usefull for supporting learning organization. 2.2. Learning Organization Peter Senge (1990) defined learning organizations as “organizations where people continually expand their capacity to create the results they truly desire, where new and expansive patterns of thinking are nurtured, where collective aspiration is set free, where people continually learning to see the whole together” (Jashpara, 2004). Almost same with Senge, Pedler, Burgoyne, and Boydell (1991) define the learning organization as “an organization that facilitates the learning of all of its members and continuously transforms itself in order to meet its strategic goals” (Yang, 2004). While Garvin (1993) said, a learning organization is “an organization skilled at creating, acquiring and transferring knowledge, and at modifying its behavior to reflect new knowledge and insight” (Vassalou, 2001). From those definitions, we conclude that in a learning orgnization, employees are encouraged and facilitated to upgrade their knowledges and skills by improve their learning capacity in order to meet organization strategic goals. There are five diciplines to implement learning organization according to Peter Senge (Senge, 2004): 1. System thinking. A framework for seeing interrelationships rather than things, for seeing patterns of change rather than static „snapshots‟. System thinking is a discipline for seing wholes. 2. Personal mastery. It is the of personal growth and learning. People with high levels of personal mastery are continually expanding their ability to create the results in life they truly seek. Organizations learn only through individuals who learn. 3. Mental model. Assumptions, generalizations, or even pictures or images that influence how people understand the world and how they take action. Managing mental models promises to be a major breakthrough for building learning organizations. 4. Building shared vision. A vision that many people are truly commited to, because it reflects their own personal vision. Shared vision is vital for the learning organization because it provides the focus and energy for learning. 3

5. Team learning. When teams are truly learning, not only are they producing extraordinary results but the individual members are growing more rapidly than could have occured otherwise. Besides five diciplines from Senge, Vassalou (2001) also defined five principles of learning organization in order the process to succeed: 1. Mission and vision. A widely shared and understood mission enables staff at all levels to develop their skills and capabilities, take reponsibilities and contribute to organizational performance 2. Leadership. Empowers employees, encourages an experimenting culture, rewards learning, supports innovative suggestions and frequently generates learning opportunities on-the-job 3. Transfer of Knowledge. Learning form past failures makes knowledge explicit and enables its transfer from individual to organizational level 4. Teamwork and co-operation. Diversity of team members‟ knowledge and backgrounds stimulates dialogue, brainstorming and team problem solving 5. Experimenting culture. Sets aside resources for employees to enagage in creative pet projects, develops rewarding mechanisms for those that excel in this area and tolerates errors. Garvin (1993) in Jashpara (2004) said that learning organization model has five main activities: systematic problem solving, experimentation with new approaches, learning from their own experience and past history, learning from the experiences and best practices of others, and transferring knowledge quickly and efficiently throughout the organization

2.3. Big Data Analytics 2.3.1. Big Data definition “Big data” refers to datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze (McKinsey, 2011). Every organization now use digital data. There are five applicable ways to leverage big data that offer transformational potential to create value an have implications for how organizations will have to be designed, organized, and managed. 1. Creating tansparency. Making big data more easily accessible to relevant stakeholders in a timely manner. 2. Enabling experimentation to discover needs, expose variability, and improve performace. Organizations can collect more accurate and detailed performance data (in real or near real time) on everything as they create and store more transactional data in digital form. 4

3. Segmenting populations to customize actions. Big data allows organizations to create highly specific segmentations and to tailor products and services precisely to meet those needs. 4. Replacing/supporting human decision making with automated algorithms. In some cases, decisions will not necessarily be automated but augmanted by analyzing huge, entire datasets using big data techniques and technologies. 5. Innovating new business models, products, and services. Big data enables companies to create new products and services, enhance existing ones, and invent entirely new business models. Big Data analytics is developed from data analytics dicipline which has been used for a long time ago for helping decision making in organization. Data analytics was used structured data with ETL (Extract, Transform, and Load) process. Some its popular applications are business intelligence, data mining, and other OLAP (Online Analytical Process) systems. There are four V‟s that characterize Big Data (Sathi, 2012): 1. Volume. With automation and digitalize data, organizations now have a huge amount of data that come from within the corporation and outside the organization. 2. Velocity. There two aspects of velocity, throuhgput of data (data moving in the pipes) and latency (using data in motion) 3. Variety. The source data includes unstructured text, sound, and video in addition to structured data. 4. Veracity. Veracity represents both the credibility of the data source as well as the suitability of the data for the target audience. 2.3.2. Big Data Application in Healthcare In healthcare industries, increasing use of multimedia has contributed significantly to the growth of big data. Image data in the form of X-rays, CT and other scans dominate data storage volumes in healthcare. While a singe page of records can total a kilobyte, a single image can require 20 tp 200 megabutes or more to store (McKinsey, 2011). Healthcare organization usually has multiple stakeholders, including pharmaceutical and medical products industries, providers, insurance agencies and patients. Each generates pools of data, but they have typically remained unconnected from each other. McKinsey Global institute define four distinct big data pools exist in the US health care domain; pharmaceutical R&D data, clinical data, activity (claims) and cost data, and patient behavior and sentiment data. Using big data analysis from that data pools could increase efficiencies, improved treatment effectivenss, and productivity enhancement (McKinsey, 2011). According to McKinsey Global Institute (2011) in healthcare industry, Big Data could be used to: 5

1. 2. 3. 4. 5. 6. 7.

Comparative effectiveness research Clinical decision support systems Transparency about medical data Remote patient monitoring Advanced analytics applied to patient profiles Automated payment systems Health economics and outcomes research and performance-based pricing plans 8. Creating new business models: Online platforms and communities 9. And supporting public health program. 3. Methodology This research uses qualitative method and exploratory approach. It‟s a case study of an internal organization of a healthcare provider. Primary data is from interview with head of service division of Yakes-Telkom. Secondary data consists of internal documents and internet references. The data was analyze by comparing study with another cases in US healthcase services to formulate a conceptual strategies of learning process using Big Data in the case study object. 4. Case Study PT. Telekomunikasi Indonesia, Tbk (Telkom) is one of the biggest state-owned enterprise in telecommunication industry. Since 1965, healthcare services had given for all employees and their families as civil officers in Indonesia. If they were sick, they could get restitution for they health care fee from the government through their institution. Until 1998, health care management for employees and pensions and their families was managed by health unit in PT. Telkom. Since April 1st, 1998 Yayasan Kesehatan Pegawai Telkom (Yakes-Telkom) was founded as separated entity from Telkom, following the policy to become go public company. Since 2000, all health care management, in regional devision, support devision, and some subsidiaries of Telkom have been managed byYakes-Telkom. Yakes-Telkom has mission to becoming the best healthcare service provider in Indonesia. To support its mission, Yakes-Telkom has establishes some strategies and policies. Some of them, that related to this paper, are increasing healthcare services and controlling expenditures. Until mid-year 2013, Yakes-Telkom has 403 employees, consits of medical and non-medical staff, spread in 950 clinics operated in 7 regions in Indonesia. Healthcare services provided inlcuding medical laboratorium, pharmacy, optic, general check-up, and some specialist. Yakes also has six internal laboratoriums under Rasapala (Yakes‟ subsidiaries) management. Yakes-Telkom has around 120.000 members from Telkom Group (including employees, pensions and their families) and around 30.000 members from Telkom subsidiaries. Yakes-Telkom has online medical record system that integrate all medical data of Yakes‟ members. The system record patients‟ personal data, treatment duration, healthcare expenditures, hospitality history, and drugs records. With this system, patients can get treatment from any clinic with any doctor without worry about their medical 6

data. All data from internal clinic (organized by Yakes) will be uploaded to medical record system by each doctors. If they take medical treatment from partner hospital, data will be input by staff when employees or pensions ask for restitutions. They should give proof of payment and medical resume from the partner hospital. From all clinics, it could reach 2560 transactions a day. To reach it goals, Yakes-Telkom has a lot of programs devided in preventive, currative, and rehabilitative programs. Preventive programs consist of health club, healthiest family award, health-life paradigm, annual medical check-up, monthly monitoring (for employees and pensions grouped as unhealthy condition). Currative programs including medical treatment in clinic and hospital. Another program is establishing Telkom‟s dugs list or Daftar Obat Telkom (DOT) for employees and pensions of Telkom. DOT is list of drugs and pharmacy materials that have selected by medical proof, save and efective. This program has objectives for drug use standardization, quality control, efficiency of claim-cost, and drug use monitoring. Beside programs for healthcare services, Yakes also has programs to increase cost efficiency. Some programs are medical treatment standardization, reducing claim cost by maximizing factory discount, reward and punishment, decreasing margin of pharmacy and hospital, drugs formulation, and regulation of healthcare facility. 5. Discussion/Interpertation of Data Yakes-Telkom has a large number of data that can be optimized to improve its processes or services. Generally, the data can be classified in three major pools. They are clinical data, patients‟ behavior data, and activity and cost data. Each data pools consists of some data sets that come from different elements. These are the detail of data pools that can be formed by Yakes. 1. Clinical data. Example datasets: electronic medical record, medical image, drugs list standard Sources: Yakes‟ clinics, partner hospitals 2. Patient‟s behavior data Example datasets: exercise data, patients behavior and preferences Source: Patients 3. Activity and cost data Example datasets: utilization of care, cost estimation Sources: Yakes‟ clinics, partner hospitals

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Clinical data

Patient's behavior data

Activity and cost data

Figure 1. Recomendation of Big Data Pools for Yakes Telkom

With those data pools, Yakes can implement some Big Data solution are to do improvement for services and processes. These are some potential improvement from big data analytics implementation, adopted from Mc Kinsey Global Institute analysis (McKinsey, 2011). 1. Comparative effectiveness research. Determining which treatments will work best for specific patients by analyzing comprehensive patient and outcome data to compare the effectiveness of various interventions. This solution can be supported by clinical data and patients‟ behavior data 2. Clinical decision support systems. Deploying clinical decision support systems for enhancing the efficiency and quality of operations. This solution can be supported by clinical data and activity and cost data. 3. Transparency about medical data. The goal is to identify and analyze sources of variability and waste in clinical processes and then optimize processes. This can be supported by clinical data and activity and cost data. 4. Remote patient monitoring. Collecting data from remote patient monitoring for chronically ill patients and analyzing the resulting data to monitor adherence (determining if patients are actually doing what was prescribed) and to improve future drug and treatment options. This can be supported by patients‟ behavior data. 5. Advanced analytics applied to patient profiles. Applying advanced analytics to patient profiles (e.g., segmentation and predictive modeling) to identify individuals who would benefit from proactive care or lifestyle changes. This can be supported by clinical data and patients‟ behavior data.

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6. Automated pricing systems. Implementing automated systems for fraud detection and checking the accuracy and consistency of patients‟ claims. This can be supported by activity and cost data. 7. Online platforms and communities. Forum for individuals can share their experience as patients in the system, or a forum for physicians to share their medical insights. This can be supported by patients‟ behavior data This table below shows how those big data solution could be used in each learning process. Table 1. Mapping of Big Data Solution and Learning Process

Learning Processs Systematic No Big Data Solution 1 2 3 4 5 6 7

Comparative effectiveness research Clinical decision support system Transparency about medical data Remote patient monitoring Advanced analytics applied to patients profiles Automated pricing system Online platform and communities

problem solving V

learning experiment from own ation experiences

learning from others

transferring knowledge efficiently

V

V

V

V

V

V

V V V

V V

V

Next step, we mapped Big Data Solutions above (from Tabel 1) to Yakes‟ Progams. Numbers in Big Data Solutions field show that those solutions support or be supported by related programs. Tabel 2. Mapping of Big Data Solution and Yakes‟ Programs

Strategies Yakes' Programs health club health family awards health-life paradigm annual medical check-up monthly monitoring currative treatment drug lists (DOT) medical treatment standardization reducing claim cost

reward and punishment decreasing margin of pharmacy & hospital drugs formulation standard regulation of healthcare facility

Increasing healthcare services V V V V V V V

Controlling expenditures

V

Big Data Solution(s) 4, 7 4, 7 4, 7 1, 5 4, 5 1, 2, 3 1, 2, 6

V

V V V V V V

2, 3 6 2 6 2, 6 2

V

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6. Conclusion and Recommendation Big data analytics can be used to improve five main activites in learning processes. In case of Yakes Telkom, those solution can also help Yakes‟ programs to increase healthcare services and control expenditures. This research still has many weakneasses. For further research, we recommend to add more data and more detail discussion about big data solutions that could be implemented in organization. 7. Bibliography [1 ] Hayes, Niall. 2011. Information Technology and Possibilities for Knowledge Sharing. Handbook of Organizational Learning & Knowledge Management, Second Edition. United Kingdom. John Willey & Sons. [2] Jashpara, Ashok. 2004. Knowledge Management, An integrated Approach. Prentice Hall. Pearson Education [3] Keidrowski, P.Jay. 2006. Quantitative assessment of a Senge learning organization intervention. The Learning Organization Vol.13 No.4, 2006 pp. 369-383. Emerald Group Publishing Limited. [4] McAfee, Andrew and Erik Brynjolfsson. 2012. Big Data: The Management Revolution. Harvard Business Review. October 2012. [5] McKinsey Global Institue. 2011. Big data: The next frontier for innovation, competitiion, and productivity. McKinsey & Company. [6] Sathi, Arvind. 2012. Big Data Analytics. Disruptive Technologies for Changing the Game. MC Press Online, LLC [7] Senge, Peter M.. 2004. Fifth Dicipline: The Art & practice of Learning Organization. New York: Doubleday. [8] Smith, Mark Easterby and Marjorie A. Lyles. 2011. The Evolving Field of Organizational Learning and Knowledge Management. Handbook of Organizational Learning & Knowledge Management, Second Edition. United Kingdom. John Willey & Sons. [9] Uriarte Jr., Filemon A. 2008. Introduction to Knowledge Management. ASEAN Foundation. Jakarta [10] Vassalou, Leda. 2001. The learning organization in health-care services: theory and practice. Journal of European Industrial Training. MCB university Press [ISSN 03090590] [11] Wahyono, Teguh. 2013. Pendirian dan Pengelolaan Yayasan Kesehatan Pegawai Telkom. Yakes-Telkom.

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[12] Yang, baiyin., Watkins, E. KAren, Marsick, Victoria J.2004. Construct of the Learning Organization: Dimensions, Measurement, and Validation. Human Resource Development Quarterly, vol. 15, no. 1, Spring 2004. Wiley Periodicals, Inc

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