MIS 9003 – Prof. Min-Seok Pang

Week 12 – Healthcare

Adjerid et al 2016 — Siddharth Bhattacharya

The paper talks about the interesting debate between privacy regulations and HIE incentives.On one hand,patient consent requirements add administrative costs and restrict the availability  of patient information.On the other a system that assumed their willingness to participate without obtaining explicit consent (i.e., an opt-out system) would not be acceptable.Thus, policy makers seeking
to foster the growth of HIE efforts face the same challenge that emerges in other industries: how to address privacy concerns without over-regulating the disclosure of personal information and stifling the growth and emergence of valuable information technology efforts reliant on it.Thus the authors want to explore  whether different forms of privacy regulation enable or impede HIE efforts.They posit that incentives could offset the significant costs associated with HIE efforts, including those that arise from varying degrees of privacy regulation.Using semiannual data from a six-year period (2004–
2009)the authors use an empirical strategy taking advantage of the fact that across different states policy makers have approached HIE challenges in different ways, enacting legislation that varied both in terms of the incentives they create for HIEs, and in terms of the types of privacy protections they afford to patient data exchanged through HIEs. The empirical investigation includes a fixed effect model(the main analysis) followed by a cross sectional model to look at underlying factors and also ruling out confounding explanations.The authors also do a series of robustness checks to rule out any endogeniety concerns.Although results show that privacy regulation without incentives had a negative effect on HIE efforts, we also find that privacy regulation, particularly regulation that includes consent requirements, was a necessary condition for incentives to positively impact HIE efforts. Incentives coupled with privacy regulation that included requirements for patient consent resulted in a 47% increase in the propensity of an HRR having a planning HIE and a 23% increase in the propensity of an HRR having an operational HIE.By contrast incentives without any privacy regulations/with privacy regualtions but which didn’t have any consent requirements resulted in little/no gain.The results contribute to literature in adoption and the diffusion of IT in healthcare—in particular, the factors and barriers that impact their adoption and is one of the first studies to examine the impact on the emergence of planning and operational HIEs of varying approaches to privacy regulation.It also contributes to the economic and policy literature evaluating the impact of privacy protections on technological progress by showing that HIE incentives consistently
offset the negative baseline effects of privacy regulation on HIEs and, more surprisingly, that incentives
were more effective in doing so when coupled with privacy regulation that included consent requirements.

Paper Summary-Jack Tong

Ayabakan, S., Bardhan, I., Zheng, Z.E. and Kirksey, K., 2017. The impact of health information sharing on duplicate testing. MIS Quarterly41(4), pp.1083-1103.

Duplicating tests in healthcare create redundant costs for both patients and the insurance providers and information sharing by integrated healthcare system could reduce duplicating tests. Because the formats and frequency of testing information storage are different between radiology tests (low volume but high cost) and laboratory tests (high volume, low cost, manually processed). The authors postulate that implementation of health information sharing technologies will reduce the duplication rate for duplicating tests, and the reduction is more salient for radiology tests compared to laboratory tests, especially when health information sharing technologies are implemented across disparate provider organizations.

The authors utilize a unique panel dataset consisting of around 40,000 patients visits to the outpatient clinics and hospitals for laboratory and imaging tests related to the diagnosis and treatment of congestive heart failure with a quasi-experimental approach. The results of the paper support the authors’ hypothesis that implementation of information sharing system could reduce the duplicating testing for patients.

Week 12 – Bhargava and Mishra 2014 – Joe

Bhargava, H. K., & Mishra, A. N. (2014). Electronic medical records and physician productivity: Evidence from panel data analysis. Management Science60(10), 2543-2562.

Contrary to decision-makers in other industries, physicians in healthcare sector perform not only knowledge work, such as making decisions and crafting treatment regimen based on patient information, but also data entry and system operation with the wide adoption of EMR. More, they are the healthcare practitioners who drive a majority of care decisions. Therefore, EMRs hold the potential to improve physician workflows and productivity, and, consequently, contain healthcare costs. This paper attempts to examine two important research questions: (1) Does physician productivity change over time as a result of EMR implementation? (2) Does this impact differ for physicians of different specialties?

Measuring physician productivity is challenging, the authors argue that using WRVUs-relative value units generated for clinical activities rather than administrative, teaching, training, or care coordination activities, to measure physician productivity-can overcome the traditional measurement drawbacks, lack of robustness and normalization. More, the theory of Task-Technology Fit(TTF) indicates the heterogeneity of the effects of EMR implementation on physician productivity. Using the dataset, which contains 3,186 physician-month productivity observations collected over 39 months may suffer from OVB, self-selection bias, and attenuation bias when constructing the OLS casual model. The authors then use a Differences-in-Differences model and Arellano–Bond GMM to relief these endogeneity concerns. Their results show that that productivity drops sharply immediately after technology implementation and recovers partly over the next few months. The longer-term impact depends on physician specialty. The net impact of the EMR system is more benign on internal medicine physicians than on pediatricians and family practitioners.

The authors find that on one hand, present-day EMR systems do not produce the kind of productivity gain that could lead to substantial savings in healthcare; at the same time, EMRs do not cause a major productivity loss on a sustained basis, as many physicians fear. Other implications and contributions are also discussed.

Week12- Reading Summary- Leting Zhang

Bhargava, H. K., & Mishra, A. N. (2014). Electronic medical records and physician productivity: Evidence from panel data analysis. Management Science60(10), 2543-2562.

This paper examines the impact of EMRs on physician productivity. There are two specific questions: 1. Does physician productivity change over time as a result of EMR implementation? 2. Does this impact differ for physicians of different specialties?

The conceptual foundations for this study mainly based on three streams of literature. The first is physician productivity, WRVUs are used to measure it. they are relative value units generated for clinical activities; the second stream is IT- Enabled productivity, extant research shows there may be significant differences between IT’s impact during short-term and long-term; the third stream is Task-Technology Fit, the paper points out the two main functions of EMRs are information review and information entry, given that physicians of different specialities have different demand for the EMRs, the productivity impacts of EMRs on them are likely  to vary.

This study uses data includes monthly physician schedule and production in a healthcare system from 2003 to 2006, in the period, EMR system was implemented across clinics. Some exploratory data analyses show EMR’s impacts on productivity are significantly different in first months and after six months, then they estimated the learning period empirically. Next, they use OLS to estimate the model which accounts for the heterogeneous in physicians and clinics. Lastly, they use  Arellano- Bond system GMM estimation which eliminates bias from unobserved heterogeneity by first-differencing and from endogeneity by using instrumental variables of available lags and levels.

Results show 1. FPs and Peds are less productive in the stable phase in comparison to IMs. The net impact of EMRs is better on IM than FPs and Peds. 2. FPs and Peds experience a decrease in productivity compared to IMs in the learning phase.

Week 12 Reading Summary – Menon and Kohli (2013) – Xi Wu

Menon, N. M., & Kohli, R. (2013). Blunting damocles’ sword: A longitudinal model of healthcare it impact on malpractice insurance premium and quality of patient care. Information Systems Research, 24(4), 918–932.

Few studies have considered its implication on product or service quality, this study fills the gap by investigate the impact of healthcare IT (HIT) expenditure on the malpractice insurance premium (MIP) and the moderating effect of HIT expenditure on the relationship between MIP and patient care quality. Based on the prior literature of IT value and risk management, three hypotheses are proposed. 1) Past HIT expenditure is positively associated with current quality of patient care. 2) Past HIT expenditure is negatively associated with MIP. 3) Past MIP is positively associated with quality of patient care. 4) The relationship between past MIP and quality of patient care is enhanced (positively moderated) by past HIT expenditure.

There are multiple data sources in this study. Hospital information is from the Washington State Department of Health. Patient care outcomes are obtained from a data services and consulting company. Three regression models are applied. To control for the likely persistence in organizational decisions and actions from year to year, lag of dependent variables is added. First-differencing method is applied to address the fixed effect factors. Some instrument variables are used to overcome the endogeneity problem. Results support all the hypothesis but H4. The moderating impact of HIT on quality of patient care through its impact on the link from past MIP to quality of patient care is negative. The findings offer opportunities for future research.

This study contributes to understanding the expectation of IT benefits and its effect on an organization. It also informs decision makers in risk, quality management, and the IT function to engage in joint risk mitigation decisions to achieve desired organizational goals.

Week 12 Reading Summary (HK)

Atasoy, H., Chen, P., & Ganju, K. (2017). The spillover effects of health IT investments on regional healthcare costs. Management Science, forthcoming, 1-20.

Past research efforts have consistently demonstrated the quality benefits associated with the implementation of electronic health records (EHR), but research considering the cost on health care is more scarce and mixed. Atasoy, Chen, and Ganju (2017) shed light onto this research gap by considering the spillover effects of health IT investments on neighbouring healthcare providers’ costs. Moreover, Atasoy et al. (2017) approached this research from a macro-level perspective by considering how one hospital implementing an EHR system impacts the costs for them as well as for  surrounding hospitals. This approach is relevant as healthcare is often a community effort with hospitals sharing patients. Thus, any variable that decreases communication costs between hospitals, such as EHR systems, will lower the financial burden for all parties in the supply chain. In order to verify this proposition, data was collated from a variety of sources including the Healthcare Information Management Systems Society database for the longitudinal period from 1998 to 2012.

Analyses considering said dataset found that though EHR system adoption increases costs at the adopting hospital, it lowers costs at surrounding hospitals. Specifically, the spillover effect is stronger when an increasing number of hospitals in the region are in health information exchange networks and in the same integrated delivery systems since these networks and systems facilitate information exchange. Moreover, for hospitals with regional characteristics that facilitate patient sharing, such as urban vs rural areas, population density, average distance between hospitals, and hospital density, the spillover effect is more pronounced. Finally, the HITECH Act, which increased the adoption and use of EHR systems, catalyzed the spillover effects. Effectively, a macro-level investigation into EHR systems indicates that they can reduce costs for collaborating hospitals.

Week 12 – Healthcare – paper assignment

Menon and Kohli (2013) Xi
Bhargava and Mishra (2014) Leting, Joe
Adjerid et al. (2016) Sid
Atasoy et al. (2017) Heather
Ayabakan et al. (2017) Jack

I am adding one more reading.

Adjerid, I., Acquisti, A., Telang, R., Padman, R., and Alder-Milstein, J. (2016) “The Impact of Privacy Regulation and Technology Incentives: The Case of Health Information Exchanges,” Management Science (62:4) pp. 1042-1063.