How would you define decision making?

Decision making like planning, organizing and/or controlling involves a process directly influenced by behavioral and scientific disciplines as we aiming to choose among two alternative courses of action to attain a goal (Sharda et al., 2015). Decision-making consists of four phases (1) Intelligence Phase– identify problems and opportunities. (2) Design Phase– access/use of data, models and analytical processing. (3) Choice Phase - evaluate the impact of propose solutions using DSS tools. (4) Implementation Phase –put recommended solution to work; collect and analyze data for future improvement.

How DSS/BI web-based technologies and tools (such as PDA, cell phone, tablet computers and laptops) aid in each phase of managerial, administrative, research and/or clinical decision making?

Decision Support System (DSS) and Business Intelligence (BI) system are meant to be an adjuncts to managerial, administrative and clinical decision makers. Simply, DSS/BI extends decision makers’ capabilities but not replacing their judgment (Sharda et al., 2015). The web-based technologies and tools are allowing decision maker(s) to utilize DSS/BI to afford effective, interactive, flexible and adaptable decision process. Through, web-based technologies and tools a real-time and a historical data, and knowledge exchange is possible. A single user or many people in different locations can collaborate a solution to a specific problem, a simultaneously. Currently, the DSS/BI system had evolved from computer based system operation into an interactively online and with a graphical output capabilities simplified by browsers and mobile devices. Through an easy user interface and an incorporation of data and decision makers’ input, the DSS/BI web-based technologies and tools support all phases of decision-making process.
Building on the example of evidence-based database for clinical decision making, cited by Deily et al. (2013), it is intriguing to find out the impact of health information technology (HIT) adoption by inpatient Hospice facility may have on the end-of-life care quality. By using the algorithm method with its fixed effects variables and system-specific linear time trends, to determine whether greater use of HIT (e.g. Medication Reconciliation List) at nonhospital (home based) Hospice facilities improves end-of-life care outcomes at Hospice’s hospitals in the same healthcare network.


Deily, M. E., Hu, T., Terrizzi, S., Chou, S., & Meyerhoefer, C. D. (2013). The Impact of Health Information Technology Adoption by Outpatient Facilities on Pregnancy Outcomes. Health Services Research, 48(1), 70-94. doi:10.1111/j.1475-6773.2012.01441.x

Sharda, R., Delen, D. & Turban, E. (2015). Business intelligence and analytics systems for decision support (10th ed). Pearson Education. Upper Saddle River: New Jersey.

Jennifer thanks for your comment.
I reviewed the decision- making phases (intelligence, design, choice and implementation) and their management support system technologies, to arrived at the following conclusion. In the intelligence phase where we examine reality, identify the problem (its ownership), and its possible support (data mining), there is not an adequate data source to justify or dispute the impact the health information technology (HIT) adoption by inpatient Hospice facility on the end-of-life care quality. However, it would be intriguing to find out, if data mining for end-of-life care becomes available, hopefully in the near future.
In our center, Hospice and palliative Care (HPC), we still struggle with the key challenges such as access to services, continuity of care and delivery of an effective and efficient patient-centered care. In our own network, Hospice@ Home, we are lacking an interoperable health information exchange (HIE) system. The lack of interface and an incorporation of data, confines decision makers’ input, as well as DSS/BI web-based technologies and tools needed for all phases of decision-making process (Sharda et al., 2015).
Shortcomings and breaches in workflow exist in our HPC institution, as we are using a very specifically designed technology systems for pain management assessment, an educational application, and team consultation software. Unfortunately, these systems are home grown systems designed in isolation from other systems, so it is difficult to assess their true viability in the context of the broader environment. I strongly believe that HIT must be built with a vision of an integrated communication system, health information exchange, including a common model of the communication and data needs, and the incorporation of data standards to ensure that data are shareable across different settings (Sharda et al., 2015). Multiple times we are require to collect the same data across different settings, wasting our time, limiting the time we could spend