While technology in healthcare has benefits and great potential, it continues to struggle with challenges. The HITECH Act was put in place to encourage healthcare facilities to adopt electronic EHR’s. With the adoption of EHR’s comes concerns with cybersecurity attacks to healthcare data and protecting patient data. Telehealth, while convenient, faces issues protecting patient data. Overall, healthcare organizations need to have a balance between using technology and protecting patient data.
Healthcare technology provides access to an abundance of data that, if utilized appropriately, can be analyzed to improve patient care, operational efficiency, and patient outcomes. The HITECH Act incentivized hospitals to adopt EHRs to improve documentation, identify trends, and use data for evidenced based decision making. While having access to an abundance of data is helpful, “Many current EHR systems reduce physician productivity, lack data sharing capabilities, and need to incorporate other key interoperability features.” {Mennemeyer et al. (2016) Having access to this data also opens risks for securing patient data, privacy, and cyber security attacks. Cybersecurity attacks can leak patient data, but can also halt operations until their demands are met. (Neprash et al. 2022) It’s important that healthcare organizations balance technology while also protecting data and privacy.
Healthcare continues to struggle with data breaches and cyber-attacks. Blockchain is a new technology that is being tested that helps secure data, reduce the risk of data breaches, and guarantee immutable, tamper-proof health data storage. Esposito et al. (2018) Benefits include avoiding performance bottlenecks, patients have more control of their data, history is complete, and changes are visible to all members of the network. (Yaqoob et al. 2022). Blockchain shows potential to share healthcare data across healthcare systems while maintaining privacy and security. Unfortunately, Blockchain still struggles with integration issues and standardized regulations. While blockchain is not ready for healthcare organizations at this time, it could potentially be part of healthcare organizations in the future.
Subject ID 10076 is an Asian male admitted with a diagnosis of lung cancer; shortness of breath. He speaks Mandarin, has Medicare insurance, and had an 8-day hospital stay. From the start, I identified a language barrier, and “Miscommunication in the healthcare sector can be life-threatening.” (Meuter et al. 2015) To obtain this information, I used the CAST function and julianday to determine length of stay in days from admission to DOD, and also used the SELECT statement to identify ethnicity, language, gender, insurance, and marital status.
This patient was admitted with a slightly elevated PCO2, which continued to rise during his hospital stay to critical levels. Elevated PCO2 is called hypercapnia which, if not reversed leads to respiratory failure. {Markou, Myrianthefs, and Baltopoulos (2004) To obtain this information joined labevents and d_labitems and using the WHERE and LIKE statement I looked for’%PCO2%’ readings. I also used valuenum > 45 to target readings outside of normal range. I did have difficulty displaying the date and time where it was easy to read. I eventually found I could angle the dates and times using element_text.
I then analyzed WBC to determine if the cause of PCO2 could be related to an infection and found that WBC’s were elevated at admission and continued to rise, indicating the patient could have an infection of some sort. Elevated PCO2 levels alongside elevated WBCs could indicate a respiratory infection. To obtain WBC count I joined labevents and d_labitems. The WHERE and LIKE statement was then used to identify the correct subject ID and ‘white blood cells’.
I also investigated sodium levels because low sodium levels or hyponatremia can also indicate infection. “Hyponatremia in the context of infections reflects the severity of the underlying disease and is associated with prolonged hospitalization and significant morbidity.” [Liamis, Milionis, and Elisaf (2011)} It was found that subject ID 10076 demonstrated a fluctuation of sodium levels throughout the admission, including readings to indicate hyponatremia. To obtain this data, I joined labevents and d_labitems and used the WHERE and LIKE statement to identify the correct subject ID and ‘%sodium%’.
The Asian population faces language barriers, especially for the elderly, who are less likely to be proficient at speaking English. “(Asians) who are not proficient in English experience barriers in help-seeking, including making an appointment, locating a health facility, communicating with health professionals, and acquiring knowledge on illness.” (Kim and Keefe 2010) Those Asians, not help- seeking are likely to not seek help until the disease or condition is further advanced. With the admission diagnosis of lung cancer and shortness of breath, it is likely that the patient was admitted with a respiratory infection. With WBCs and PCO2 continuing to rise and hyponatremia during admission, it is likely that the patient became septic from the respiratory infection, resulting in respiratory failure and death.
SELECT CAST(julianday(DOD) - julianday(admittime) as integer) as stay_length, admissions.subject_id, ethnicity, language, DOD, admittime, gender, insurance, diagnosis
FROM admissions
INNER JOIN patients
ON admissions.subject_id = patients.subject_id
WHERE admissions.subject_id = 10076
ORDER BY stay_length | stay_length | subject_id | ethnicity | language | dod | admittime | gender | insurance | diagnosis |
|---|---|---|---|---|---|---|---|---|
| 8 | 10076 | ASIAN | MAND | 2107-03-30 00:00:00 | 2107-03-21 21:16:00 | M | Medicare | LUNG CANCER;SHORTNESS OF BREATH |
SELECT label AS lab_name, value, valuenum, flag, subject_id, charttime
FROM labevents
inner join d_labitems
on labevents.itemid = d_labitems.itemid
WHERE subject_id = 10076
and label LIKE '%sodium%'
ORDER BY charttime ggplot(data = myq100,
aes(x = charttime, y = valuenum ))+
geom_line(color = "green") +
geom_point(color = "red") +
labs(
title = "Sodium Value- Normal Range: 135-145",
x = "Date/Time",
y= "Sodium"
) +
theme(
axis.text.x = element_text(angle = 45, hjust = 1))`geom_line()`: Each group consists of only one observation.
ℹ Do you need to adjust the group aesthetic?
SELECT label AS lab_name, value, valuenum, flag, subject_id, charttime
FROM labevents
inner join d_labitems
on labevents.itemid = d_labitems.itemid
WHERE subject_id = 10076
and label LIKE '%white blood cells%'
and valuenum > 16.8
ORDER BY charttime ggplot(myq1, aes(x = charttime, y = valuenum)) +
geom_point(color = "red") +
labs(
title = "White Blood Cell Count Over Time",
x = "Time",
y = "WBC"
) +
theme_minimal()
SELECT label AS lab_name, value, valuenum, flag, subject_id, charttime
FROM labevents
inner join d_labitems
on labevents.itemid = d_labitems.itemid
WHERE label LIKE '%PCO2%'
and valuenum > 45
and subject_id = 10076
ORDER BY charttime ggplot(myq12, aes(x = charttime, y = valuenum)) +
geom_col() +
labs(
title = "PCO2 Level- Normal Level: 35-45",
x = "Time",
y = "PCO2",
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1))
My recommendations for the industry to protect privacy are that healthcare administrators provide staff training and create a culture that emphasizes the importance of privacy protection. Alongside creating a culture, I would recommend that staff be held accountable for not adhering to privacy policies. “Employee carelessness, deliberate violations, and inadequate training all play a role in creating weaknesses in the healthcare information system. That’s why it’s essential to prioritize education and training initiatives for healthcare staff. (Turkstani et al. 2025) When creating EHR access settings, role-based access should be implemented to prevent staff from accessing more information than needed to perform their jobs. I also recommend that audits be done periodically to help identify if there has been a potential data breach. (Turkstani et al. 2025)