Impact of MU_EHR when Task of Treating Patients is More Comple

Task complexity has been identified as an important factor affecting performance in various settings such as organizational studies, information seeking studies, psychological studies, etc. (Payne 1976, Van de Ven and Ferry 1980, Locke et al. 1981, Culnan 1983, Campbell and Gingrich 1986, Wood 1986, Campbell 1991, Argote et al. 1995). Task complexity determines the information processing behavior, cognitive load and decision making process of a person or a team (Campbell, 1988), and is often divided into objective and subjective task complexities. Liu and Li (2012) provide a review on task complexity.

In our paper, we focus on objective task complexities that are related to task characteristics, and independent of the characteristics of the person who performs the task. Task complexity can arise due to several factors: the number of distinct acts and information cues that have to be processed, the amount of coordination required, and the relationship between task inputs that have to be taken into account in order to complete the task (Wood, 1986; Campbell, 1988). As the complexity of the task increases via the amount of information that needs to be processed, it puts a larger information load on a person’s memory and attention. It forces humans to put greater cognitive resources to use in such situations, which in turn may force people to either make tradeoffs between the time required to make a decision and the decision accuracy, or to make suboptimal decisions (Johnson and Payne, 1985; Milkman et al., 2009).

In our study, we consider treating the patient as the main task. Given that patient diagnosis and treatment are complex tasks, several mental processes have to be synchronized in order to provide the best outcomes. In healthcare, complexity can arise in

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various forms. For example, performing a coronary artery bypass grafting procedure is more complex than treating a fractured bone. The former involves more critical steps such as ensuring that the correct drugs are administered before and after surgery, the necessary checklists are followed, correct tests are performed before and after surgery, etc. Thus more complex procedures require care providers to access and keep track of a greater amount of information. As the amount of patient information that needs to be processed increases, the knowledge and memory requirements to perform the task also increases because the care provider must process all this information at once and arrive at the best course of action (Chandler and Sweller, 1991). Presence of comorbidities such as hypertension, diabetes and obesity are known to increase post-operative complications and discharge decisions in patients undergoing shoulder, hip or knee surgery (Jain et al. 2005). When care providers encounter such patients, their treatment decision will depend on several factors such as selection of the right treatment drugs, deciding the correct amount of drug dosage, the method of drug administration, potential drug interactions, and the duration of the treatment. Thus cognitive requirements would increase as comorbidities that a person arrives with increases. Finally, some tasks require greater coordination in the timing and sequencing of activities than others (Wood 1986, Braarud and Kirwan 2011). As the steps involved in performing a task become more interconnected, people who perform the later steps in the execution of a given task will have to learn based on the information provided by the previous steps. The right information on treatment plans, dosing schedules, and other protocols have to be communicated to all parties involved to avoid errors such as performing surgery on the wrong part of the body, overdosing, etc. (Seiden and Barach, 2006). Further, treatment

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notes and instructions written by various clinical specialists have to be made available to all care providers, especially during handoffs (Solet et al., 2005). Thus greater coordination implies that greater amount of information has to be formally exchanged among the involved agencies.

Typically, doctors and nurses are trained to diagnose problems and identify patterns of symptoms, and this ability gets better with experience (Elstein and Schwarz, 2002). However, when a clinician encounters complex cases, it is possible that his or her ability to make good decisions becomes compromised under high cognitive load. This can lead to poorer decision-making, improper medications, failure to treat all accompanying conditions, etc. (Burgess, 2009; Parchman et al., 2007; Redelmeier et al., 1998). This issue is further complicated in hospitals where providers encounter patient heterogeneity, and may have to accommodate interruptions and unscheduled requests that may increase the time required to complete the job as providers have to revisit task details (Froehle and White 2013).

Previous literature has shown that (1) The correct fit between task and technology is critical in predicting the success of information technology and that (2) Fit is determined by the interaction between the characteristics of task and technology (Cooper and Zmud, 1980, DeSantis and Poole, 1994, Goodhue 1995, Goodhue and Thompson 1995, Zigurs and Buckland 1998, Dennis et al. 2001, Banker et al. 2002). It has also been suggested that group decision support systems technology may work better for complex tasks as compared to simple tasks (Dennis and Gallupe, 1993). In the case of complex tasks, technology that provides rich information, clarifies task assignment, supports communication, and enables feedback results in better performance (Andres and Zmud,

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2002; Gebauer et al., 2005; Ren et al. 2008). A pioneering study conducted by Autor et al. (2003) on the effect of computerization across multiple industries suggests that information technology can “complement workers in executing non-routine tasks demanding flexibility, creativity, generalized problem-solving capability and complex communications”. Mapping these results to the healthcare settings, it is plausible that key features of EHRs such as checking for drug-drug or drug-allergy interactions, referring to treatment guidelines, ordering additional tests, communicating with other physicians, etc., that are mandated to be used in a meaningful way, may possibly add more value when tasks are non-routine and more complex as argued by Autor et al. (2003). We can view MU_EHR as a group decision support system comprising of a set of technologies such as CDR, CDSS and CPOE. As information on disease conditions, treatment protocols, and checks for drug-drug and drug-allergy interactions are embedded into these group support systems, providers can quickly and easily navigate this knowledge database for more complex tasks and accrue larger benefits for higher complexity patients. Thus we hypothesize:

H2: While meaningful use of EHRs will reduce length of stay for all patients, the magnitude of effect will be larger for high complexity patients than for low complexity patients