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How Economical are Data & Analytics for Healthcare Providers?

What decision-makers in hospitals and practices should look out for when investing in data and analytics technologies

Philip von Wedel / Christian Hagist - February 17, 2021

Tips for practitioners

It is by no means a secret that the digitalization has changed many, if not all, areas of our lives. Most industries pivoted towards digitally enhanced or fully digital offerings since this improved consumer experiences and hence drove demand. In healthcare, however, these market-driven processes are often weakened due to the strongly regulated nature of the industry. As a result, healthcare providers apparently did not feel the need to significantly raise the digitalizationbudgets.

European healthcare providers are playing catch up

In 2017, for example, almost 50% of hospitals in Germany and Austria did not have an ElectronicHealthRecord (EHR) for their patients but relied on paper-based documentation. At this point one might ask: But do these data and analytics technologies actually improve the quality of our healthcare? Do we actually need to invest in digitalization here? The answer may not come as a surprise: Yes! Latest research shows that adoption of EHRs leads to fewer medication errors, less adverse drug effects, and higher guideline adherence. Based on EHR data, Advanced Analytics (AA) involving Artificial Intelligence (AI) is already able to predict the onset of several diseases as well as provide care-related forecasts. One of the more recent topics is the possibility to diagnose the novel coronavirus disease (COVID-19) by applying AI to chest computed tomography scans.

Ironically, in a world that has not yet significantly pivoted towards quality-based reimbursement, quality improvements via data and analytics are not necessarily directly linked to economic benefits for single healthcare providers. As a result, healthcare providers see the initial and ongoing maintenance costs as key barriers for adoption and oftentimes question overall cost-effectiveness of these solutions. In the United States, policy makers successfully tackled this issue already in 2009 via strong financial subsidies pushing EHR adoption to almost 100% today. In Germany, the recent 2020 Hospital Future Act (“Krankenhauszukunftsgesetz”) injects up to €4.3 billion in digital infrastructure projects in order to accelerate overdue investments. The question pertains whether healthcare providers can actually derive economic value from investments in data and analytics. We comprehensively investigated this question in a recently published literature review and want to elaborate on the most important and practically relevant insights in this article.

Keep five technology categories in mind for investment decisions

We identified five key technology categories to be considered for data and analytics investment decisions: Electronic Health Records, Computerized Clinical Decision Support, Advanced Analytics, Business Analytics, and Telemedicine (see table). The impact on provider economics varies depending on category but overall, roughly 60% of identified research articles indicate a positive impact. Firstly, the EHR represents the most thoroughly researched technology for healthcare providers. However, only 12 of the 30 identified articles indicate a positive economic impact related to an EHR installation.

Unfortunately, this finding on its own does do not necessarily promote a quick decision on EHR investments, at least from an economic point of view. An observation that is clearly mirrored by reality, where oftentimes only heavy government subsidies are able to drive adoption. However, we identified several factors that increase the probability for a timely break-even. Eliminating all legacy costs like paper-based records and related dictation services, repurposing paper record space into clinical space, or installing new technology in a stepwise fashion (avoiding a big bang) are all important factors here. Fortunately, research paints a more positive picture for the remaining four categories. Computerized Clinical Decision Support (CCDS) tools can save material and labor costs by avoiding redundant laboratory tests and imaging studies. Business Analytics tools analyzing operational workflow or billing and claims data can improve efficiency or drive revenue by identifying missed claims. Thirdly, Advanced Analytics tools that rely on AI and Big Data analytics can lead to cost savings by utilizing mainly health data from EHRs to support diagnosis, decision making or workflows. Lastly, telemedical applications like remote monitoring also facilitate cost savings in certain cases. All in our review identified research concerning these last four technologies indicated only positive economic impacts for providers either via saving costs or increasing revenue.

Look beyond the Electronic Health Record

At this point, it is important to consider these five data and analytics categories not just individually but jointly. For example, CCDS and Advanced Analytics involving AI, in most cases, rely on high quality data provided by an EHR. EHR adoption hence can become a bottleneck to the positive economic effects of technologies further down the line. Not installing an EHR due to ambiguity around cost-effectiveness in fact locks the door towards economic benefits from the adjoined technologies. The EHR acts here as a very important door opener. This again amplifies the importance of subsidies for EHR installations by governments which should always be utilized by providers when available in the respective country. However, these findings also point at the fact that each investment case of installing an EHR should always consider the second-order economic effects of the adjoined technologies. In many cases, looking beyond just the EHR can make difficult investment decisions easier or even shorten break-even timelines. Data and analytics tools in healthcare cannot be considered individually but always in interplay. Our research shows that this is also the case when considering their economic impact on providers.

Tips for practitioners

  • To optimize cost-effectiveness when installing a EHR in your organization, avoid keeping legacy analog structures (e.g., paper-based records), install the latest available software with flow diagrams and do so in a stepwise fashion avoiding a big-bang transition.
  • Look beyond the EHR and also consider mainly economically beneficial technologies like Computerized Clinical Decision Support (CCDS), Business Analytics, Advanced Analytics (e.g., involving AI) and telemedical applications.
  • Do not consider the EHR in isolation, but be aware of the positive economic effects of the adjoined technologies like CCDS and Advanced Analytics – this might have a significant impact on your investment case and decision.
  • Educate yourself about and make use of governmental subsidies for provider data & analytics in your country (e.g., the Hospital Future Act (KHZG) in Germany).

Literature references

Original publication

Further reading

  • Campanella, P./Lovato, E./Marone, C./Fallacara, L./Mancuso, A./Ricciardi, W., et al. (2016): The impact of electronic health records on healthcare quality: a systematic review and meta-analysis, in European Journal of Public Health, Vol. 26 (1), S. 60-64
  • Chaudhry, B./Wang, J./Wu, S./Maglione, M./Mojica, W./Roth, E., et al. (2006): Systematic review: Impact of health information technology on quality, efficiency, and costs of medical care, in Annals of Internal Medicine, Vol. 144 (10), S. 742-752
  • Miotto, R./Li, L./Kidd, BA./Dudley, JT. (2016): Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records, in Scientific Reports (6)
  • Desautels, T./Calvert, J./Hoffman, J./Jay, M./Kerem, Y./Shieh, L., et al. (2016): Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach, in JMIR Medical Informatics, Vol. 4 (3)
  • Li, L./Qin, L./Xu, Z./Yin, Y./Wang, X./Kong, B., et al. (2020): Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy, in Radiology, Vol. 296 (2)
  • Kruse, CS./Kristof, C./Jones, B./Mitchell, E./Martinez, A. (2016):  Barriers to Electronic Health Record Adoption: A Systematic Literature Review, in Journal of Medical Systems, Vol. 40 (12)
  • Blumenthal, D. (2010): Launching HITECH, in New England Journal of Medicine, Vol. 362 (5), S. 382-385

Authors of the study

MSc. Philip von Wedel

Philip von Wedel received his BSc and MSc in business administration from WHU – Otto Beisheim School of Management and is currently a doctoral candidate in Digital Health Economics at the Chair of Economic and Social Policy there. His research examines the impact of data & analytics tools on the quality and efficiency of healthcare delivery.

Professor Christian Hagist

Christian Hagist is Professor for Economics and holds the Chair of Economic and Social Policy at WHU – Otto Beisheim School of Management. His research is centered around intergenerational economic policies including pension systems (intergenerational contracts) and health policies. In the past few years, Christian has been invited to present his research to health practitioners and politicians at various levels (state and federal parliaments).

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