Analysis Report Three - Information Systems and Healthcare Provider Interactions

Executive Summary

Electronic Health Records and Electronic Medical Records can improve healthcare by making patient information easier to access, organize, and share. However, these systems also change the daily work of non-technical healthcare workers, especially caregivers, nurses, and support staff. This report explains how hospitals can make EHR implementation less stressful by involving caregivers early, providing strong training, and using managers as change leaders. The assigned readings show that EHR adoption is not just a technology issue; it is also an emotional and organizational transition. The data analysis uses the MIMIC-III caregivers table to show how EHR data can make caregiver roles and documentation patterns more visible. These insights can help hospitals improve communication, workflow planning, and patient care.

Introduction

Hospitals rely on large amounts of patient information every day. Electronic Health Records help organize this information so that providers can see patient history, documentation, test results, and care team activity in one place. This can improve patient safety and coordination, but the transition from older systems or paper-based workflows can be difficult for caregivers. McAlearney et al. describe EHR implementation as a process that can feel like grief because workers may feel they are losing familiar routines, professional confidence, or control over their daily work (McAlearney et al. 2015). This is why hospitals must treat EHR adoption as a people-centered change process, not just a software rollout.

Research also shows that EHR systems can create both benefits and burdens. Tsai et al. found that EHR implementation can improve access to information and communication, but barriers such as poor usability, workflow disruption, and training problems can limit adoption (Tsai et al. 2020). For caregivers, the main issue is not whether technology exists, but whether it actually supports the work they do with patients. If EHRs are implemented without enough training or feedback, caregivers may feel that the system adds stress instead of helping them provide care.

The Healthcare Context

The healthcare context for this report is the implementation of EMR and EHR systems among non-technical healthcare workers. Caregivers and nurses are often responsible for documenting observations, communicating with other departments, following alerts, and helping patients and families understand the care process. When an EHR system is designed well, it can support this work by making important information visible across the hospital. When it is designed poorly, it can increase documentation burden and create frustration.

One of the assigned readings, “Seven Years after Meaningful Use, shows that physicians and nurses had mixed experiences with EHRs after years of federal investment. The technology improved access to patient information, but it also created documentation workload and workflow challenges (Rathert et al. 2019). This matters because non-technical healthcare workers are usually the people who experience these workflow changes most directly. They are expected to keep caring for patients while also learning a new system.

The “Journey Through Grief” reading is especially important because it explains why employees may resist an EHR even when the system is supposed to help. Resistance does not always mean workers are being difficult. Sometimes it means they are reacting to the loss of old routines, comfort, and confidence (McAlearney et al. 2015). Managers can reduce this grief by listening to concerns, explaining why the system matters, giving hands-on practice, and making support available after go-live.

The Wall Street Journal article “When Al Overrules the Nurses Caring for You” also connects to this issue because it shows the risk of allowing technology to override caregiver judgment (Bannon 2023). EHRs and Al alerts should support caregivers, not replace their clinical experience. A strong EHR implementation should give caregivers better information while still respecting their role in noticing patient changes that may not be obvious in the data.

Data Visualizations

The following visualizations use the MIMIC- Ill caregivers table. These examples show how EHR data can make caregiver roles and documentation activity more visible to healthcare leaders.

Visualization One

This first visualization uses the caregivers table to count the most common caregiver labels recorded in the database. The purpose of this graph is to show how an EHR can help hospitals understand which caregiver roles appear most often in the care record. This type of visibility matters because hospitals need to know who is most involved in documenting and supporting patient care.

SELECT label,
       COUNT(*) AS caregiver_count
FROM caregivers
WHERE label IS NOT NULL
GROUP BY label
ORDER BY caregiver_count DESC
LIMIT 10

ggplot (data = myquery1,
aes (x = reorder (label, caregiver_count),
y = caregiver_count)) +
geom_col () + coord_flip() + labs (
title = "Most Common Caregiver Roles in the EHR",
x = "Caregiver Role",
y = "Count"
)

This graph helps show that caregiver information is not just background data. It can help managers understand who is represented in the record and which roles are most visible in the EHR. For example, if certain caregiver roles appear more often, because these caregiver roles appear most frequently in the EHR, hospitals should prioritize them during implementation. Training these high-use caregiver groups first can reduce documentation errors, improve adoption, and make the transition smoother for the rest of the organization. (Tsai et al. 2020).

Visualization Two

The second visualization connects caregivers to charted patient events. This query joins caregivers with chartevents using the caregiver ID. The goal is to show which caregiver roles are connected to the most documentation activity. This matters because EHR implementation can change workload, and hospitals need to understand which workers are most affected by documentation expectations.

SELECT caregivers.label,
COUNT(chartevents.row_id) AS chart_count
FROM caregivers
JOIN chartevents
ON caregivers.cgid = chartevents.cgid
WHERE caregivers.label IS NOT NULL
AND TRIM(caregivers.label) != ''
AND TRIM(caregivers.label) != '-'
GROUP BY caregivers.label
ORDER BY chart_count DESC
LIMIT 10

ggplot(data = myquery2,
       aes(x = reorder(label, chart_count),
           y = chart_count)) +
  geom_col() +
  coord_flip() +
  labs(
    title = "Caregiver Roles by Charting Activity",
    x = "Caregiver Role",
    y = "Number of Charted Events"
  )

This visualization shows how EHR data can reveal workload patterns that may not be obvious from the outside. If certain caregiver roles are connected to more charting activity, then those workers may experience the biggest impact when a new EHR system is introduced. This supports the idea that managers should not treat training as one-size-fits-all. Instead, they should identify which roles document the most and provide those workers with extra practice, peer support, and workflow adiustments. Research on documentation burden shows that EHR work can contribute to stress and burnout when systems are not designed around clinical workflow (Cho et al. 2024; Alobayli et al. 2023).

The chart clearly shows that Registered Nurses (RN) are responsible for substantially more charting activity than other caregiver groups. This suggests that workflow changes, training, and documentation improvements should focus heavily on nursing staff because they experience the greatest interaction with the EHR.

Recommendations for Industry

Hospitals should approach EHR implementation as a change-management process instead of only a technology project. Managers should involve caregivers early by asking them what parts of the current workflow work well, what causes frustration, and what they need from the new system. This helps reduce resistance because caregivers are more likely to accept technology when they feel included in the decision-making process.

Hospitals should also use role-based training. The caregiver roles that appear most often in the EHR or complete the most documentation should receive more hands-on practice before go-live. Training should include realistic patient scenarios, not just basic software demonstrations. This matters because caregivers need to understand how the system fits into their actual daily responsibilities.

Another recommendation is to create a super-user support system. Super users are experienced staff members who help coworkers during and after implementation. This can make the transition feel less intimidating because caregivers can get help from someone who understands both the technology and the clinical environment. Managers should also keep communication open after launch by collecting feedback and adjusting workflows when problems appear.

Finally, hospitals should protect caregiver judgment. Technology should support clinical decisions, not replace human observation. The Al reading shows that nurses can experience moral distress when technology forces them to follow an alert that does not match what they see at the bedside (Bannon 2023). Managers should create clear policies that allow caregivers to question alerts, escalate concerns, and document clinical reasoning.

References

Alobayli, Fatimah et al. 2023. “Electronic Health Record Stress and Burnout Among Clinicians: A Systematic Review.” JMIR Medical Informatics.

Bannon, Lisa. 2023. “When AI Overrules the Nurses Caring for You.” The Wall Street Journal.

Cho, Hyejin et al. 2024. “Electronic Health Record System Use and Documentation Burden Among Acute and Critical Care Nurses.” Journal of Medical Internet Research.

McAlearney, Ann Scheck, Jennifer L Hefner, Cynthia J Sieck, Moriah K Rizer, and Timothy R Huerta. 2015. “The Journey Through Grief: Insights from a Qualitative Study of Electronic Health Record Implementation.” Health Services Research 50 (2): 462–88.

Rathert, Cheryl, T H Porter, Jessica N Mittler, and Michelle Fleig-Palmer. 2019. “Seven Years After Meaningful Use: Physicians’ and Nurses’ Experiences with Electronic Health Records.” Health Care Management Review 44 (1): 30–40.

Tsai, Chi-Hsuan, Aboozar Eghdam, Nadia Davoody, Gabriela Wright, Stephen Flowerday, and Sabine Koch. 2020. “Effects of Electronic Health Record Implementation and Barriers to Adoption and Use: A Scoping Review and Qualitative Analysis of the Content.” Life 10 (12): 327.