opportunities and challenges for utilization of clinical data

Phase 1 Data Collection

· Describe the opportunities and challenges for utilization of clinical data.

· Apply the framework for conceptualizing data usage in healthcare.

· Select the correct answer with font size 14 with an explanation in 2 or 3 sentences

Q 1

Part 1.

How are images commonly represented when given to a deep learning model?

As a 1-dimensional vector, where each number is a hand-picked feature

As layers of number grids, where each number is pixel intensity

As a sequence of 1-dimensional vectors, where each number is pixel intensity

As a 1-dimensional vector, where each number is pixel intensity

Part 2.

What deep neural network architecture is most commonly used for image classification?

Recurrent Neural Network (RNN)

Multi-layer Perceptron (MLP)

Generative Adversarial Network (GAN)

Convolutional Neural Network (CNN)

Part 3.

What is the kind of question being answered via the COVID detector?

Multi-label classification

Binary classification

Sequence-to-sequence translation

Linear regression

Part 4.

You are interested in further leveraging hospital resources in order to boost the performance of your COVID detector. Which of the following actions would improve the likelihood of a high performing model? Check all that apply.

Giving the machine learning team segmentation labels for a small subset of the COVID chest x-ray dataset.

Giving the machine learning team access to an existing COVID detector.

Giving the machine learning team a large dataset of chest x-rays, even if they do not originate from COVID-positive patients.

Giving the machine learning team the text reports associated with each of the COVID chest x-ray examinations.

Q 2

Part 1.

How can we represent a patient’s electronic health record, a form of structured data, to a machine learning model?

As layers of number grids, where each number is pixel intensity

As a 1-dimensional vector, where each number is a hand-picked feature

As a 1-dimensional vector, where each number is pixel intensity

As a sequence of 1-dimensional vectors, where each number is pixel intensity

Part 2.

Given the type of data available, which of the following are reasonable alternative framings of task at hand, from a machine learning perspective? Check all that apply.

A regression model that predicts the patient’s date of death.

A model that predicts the number of days before a patient requires invasive mechanical ventilation. This model would be trained only on patients who required invasive mechanical ventilation.

A binary classification model that predicts whether or not the patient will require hospitalization.

A model that predicts what range of days it will take for a patient to require invasive mechanical ventilation. The 4 categories include: [“0-4 days”, “5-9 days”, “10-14 days”, “14+ days or will not need one”]

Part 3.

Given that we are training a model to predict whether or not the patient requires invasive mechanical ventilation, which of these values should NOT be passed into the model as a feature? Check all that apply.

Ferritin

Invasive mechanical ventilation date

Patient birth date

D-DIMER

White Blood Cell count

Ventilator setting

Patient inpatient arrival date

Part 4.

Imagine the path that the patient data took through the healthcare system. What are some possible errors that might have gotten introduced to the data before it was published? Check all that apply.

The patient was a recent transfer from another system

The patient comes to ED and gets immediately intubated, thus no labs are provided

Labs are logged AFTER the invasive mechanical ventilation

Describe the opportunities and challenges for utilization of clinical data.

Introduction

Clinical data is the goldmine of information that can be used to improve healthcare. It provides a comprehensive picture of patients and enables us to make better decisions about their care. However, there are challenges associated with utilizing clinical data, including limited access and organizational structure. In this article we will discuss some of these challenges so that we can learn how best to tackle them when implementing new initiatives within our organizations’ clinical environments.

Data are collected by many different methods across multiple care delivery settings.

Data are collected by many different methods across multiple care delivery settings. Some examples include:

• Electronic medical records (EMRs) – These records are usually stored in a hospital’s EMR system and typically contain information about an individual’s medical history and treatment, as well as information about their health status. Patients may also be able to access their own EMRs online.
• Patient portals – These websites allow patients to view their personal health record, view test results and access other services provided by hospitals or clinics that they visit regularly. They can also sign up for email updates regarding specific conditions they might have so that they’re aware when it’s time for follow-up appointments or checkups at a particular location within the clinic network itself!
• Wearables – These devices (such as Fitbit) track physical activity levels throughout each day; some even monitor sleep patterns too! In addition, wearables such as smartwatches enable users

Data within the clinical environment are often in the form of unstructured text, images and video.

Unstructured data is a term used to describe unstructured text, images, video and audio. The concept of unstructured data encompasses all types of information that are not stored in the traditional database format of structured data. Unstructured information can be challenging to use because it doesn’t have a clear structure or schema (rules or structure). However, unstructured information has many uses including:

• Data cleansing – When you need to clean up messy patient records so they can be easily understood by other doctors who may be reading them years later
• Data integration – When patients share medical records with their doctors through electronic health records systems without having access directly through those same systems

The clinical data is accessed by a small subset of individuals who have access to it.

As a clinician, researcher or IT staff member, you may have access to clinical data. However, the data is often not accessible by patients or their families.

This can be due to security controls or lack of training on how to use the information contained in these records.

The organizational structure of healthcare organizations can make it difficult to share information with other entities.

The organizational structure of healthcare organizations can make it difficult to share information with other entities. The lack of interoperability between different systems and the inability to share data across borders is one challenge that has been experienced by many organizations. Another issue is that there is no common language for sharing data with other entities, which makes it difficult for individuals within an organization to collaborate on a project or even transfer information from one system to another.

There are many opportunities and challenges associated with utilizing clinical data.

There are many opportunities and challenges associated with utilizing clinical data.

Opportunities:

• Improved patient care: Clinical data can be used to improve patient outcomes, such as reducing hospital-acquired infections or mortality rates. The additional information will allow doctors to tailor treatment plans based on their patients’ needs and preferences. For example, if a patient has a high risk of developing pneumonia after surgery due to the type of anesthesia used during surgery, then this information could be used by health care professionals so that they know how best to monitor their condition in order for them not only survive but recover well from any complications related thereto (e.g., heart failure). This would ultimately lead towards better outcomes for both parties involved as well as increased efficiency within healthcare systems overall due simply because there’d be less wasted resources spent over time when trying new things instead just sticking with what works best right now without considering future possibilities down line which may end up costing more money later down line depending upon how effective those decisions turn out being…

Conclusion

There are many opportunities and challenges associated with utilizing clinical data. The ability to access clinical data in a timely manner and share it across care delivery settings will improve patient outcomes and reduce healthcare costs. Healthcare organizations that put in place the right IT infrastructure and processes can leverage this data to improve their operations, provide better care for their patients, and protect themselves against cyberattacks.