MBA 673 Info Systems in Healthcare

MBA 673 Info Systems in Healthcare

Instructions:

MBA 673 Info Systems in Healthcare

I’m working on a health & medical question and need an explanation and answer to help me learn.

Quickly read the following two WSJ articles.

A Cloud Based Cure for Healthcare

Google AI Shows Promise

These articles discuss some very interesting technological opportunities for healthcare. However, implementing these technologies may not be easy. Discuss these technologies in light of the knowledge you’ve gained so far in the class. Specifically, discuss the challenges that may make implementing the benefits discussed in these articles difficult. You’re welcome to use any resources from the course so far. You might talk about their analytics requirements, EHR compatibility challenges, “New Rules for IT” or any other topics that are interesting to you.

2. A Cloud Based Cure for Healthcare

A new, connected health care model requires new, cloud-based platforms. Medical technology companies that partner with other industries, government regulators, and each other to build them may reap substantial business benefits from the resulting transformation.

Since 2010, the U.S. government has committed more than $33 billion in taxpayer dollars to digitizing national health care records¹ and private companies have spent another $14.5 billion producing health-related mobile and digital applications. ² but despite these high levels of investment, the health care industry has not drastically improved patient outcomes through information technology.

There’s no doubt advancements in technologies, competitive pressures to innovate, and changing payment models could drive transformative change in the health care industry, in much the same way they have revolutionized others. With the adoption of smaller computer processors, better sensors, and advanced analytics, medical technology and device companies have the opportunity to put more data collection and computer-based intelligence closer to patients. One need only look at the recent explosion of consumer-based wearables like the FitBit or the Apple Watch to get a hint of what clinical applications could do.

The truly transformative effects of technology, however, will only emerge from a connected health care model. And while the Internet of Things, big data, advanced analytics, and in-memory computing are all key components of such a system, the beating heart of this model will be new cloud-based platforms.

Growing volumes of patient data are being produced by a wide variety of sources. By mining and analyzing that varied information in real time, caregivers will be able to create coordinated, customized lifestyle and proactive treatment plans that can follow a patient throughout his or her health care journey.

As the industry considers adopting more outcome-based payment models, the connected care model could facilitate a shift in focus from treatment to prevention. Such a model will also enable individuals to manage and receive care independent of a health care facility, a much-needed advance given the looming supply-side shortages in the industry.

The benefits could extend beyond individual treatment. For example, by accessing a patient’s pacemaker data during a clinical trial, a pharmaceutical company could electronically monitor, in real time, how the drug affects the heart and determine possible side effects. That real-time analysis could dramatically reduce the industry average 12 years and $350 million it currently takes to bring a drug through the R&D process to the pharmacy. ³

Realizing the benefits of connectivity must begin with replacing the legacy technology platforms that have held the industry back. Progressive medical technology companies can become trendsetters, shaping the platforms that will form the core of connected care. They’ll become more than just producers and consumers of data; they could also become brokers of health care insights. As manufacturers of devices that sit in the middle of the patient value chain, they are well-positioned to build the platforms to integrate, analyze, and utilize health data.

How It Works Today

There are already a variety of wearable and implanted medical devices in use today, from EKGs to insulin pumps. Such devices typically consist of a power source, a set of sensors or components for interacting with the patient (such as visual cues, electrical response, or drug delivery mechanisms, for example), and data transmission and receiver components. Few of these devices have any embedded computer intelligence and they rely on standard radio frequencies or Bluetooth low energy for data transmission.

Decentralized, hospital-based medical device data hubs are the center of many legacy medical device platforms. To share data, patients must visit a provider’s office where medical technicians can download device data and update operating parameters. Only once the hub has aggregated and processed patient data can it be transmitted to a locally networked electronic health records (EHR) system.

This device data is used retrospectively by physicians to inform a patient’s course of treatment, but is rarely combined with other disparate data sources to support more customized treatments or proactive identification of health issues.

Coordination across data platforms, key security and regulatory challenges, and patient adoption concerns are all partially to blame for the slow progress of connected care. Because there are so many independent producers and consumers of data and many unique restrictions around data privacy and use, no single party has been able to effectively apply the advances in cloud computing technology and data analysis to health care.

A Collaborative, Cloud-Based Approach

Medical technology companies are poised to bridge that innovation gap, and many recognize the potential strategic benefits of the connected care evolution. The nature of the industry and its constantly shifting security and regulatory challenges have limited their ability to become early adopters in the past. However, the availability of digital patient data and recent proliferation of analysis tools have lowered the technology hurdle. Combine that with health care executives’ recognition that device and drug price premiums must fall in an industry shifting toward patient outcome-based payment, and the incentives exist for forward-thinking CIOs to focus heavily on the technology enablers for health care data and insights.

Medical technology companies who want to be part of this innovation will have to make changes both internally and externally. They must be willing to rethink their own IT infrastructure, applications, and technology strategy and collaborate within the industry sector as well as with stakeholders in newly adjacent industries, namely, consumer electronics and mobile software.

Ultimately, all of these efforts will enable companies to harness data from disparate sources into a common, consumable, cloud-enabled platform, improve security and data protection, and determine how to best utilize health data for the greater benefit of innovation.

A key component will be secure and reliable exchange of standardized health care data to facilitate coordinated patient care. The cloud can be used as a platform to collect and combine patient medical device data with EHR, medical imaging, pharmacies, and other devices to produce an end-to-end system. This will provide complete visibility for all parties: providers, patients, medical technology companies, and regulatory agencies.

The enterprises that want to take a leadership position in developing these shared platforms will take a new approach in four areas:

Share investment. Part of the value proposition for integrated cloud consortiums is distributing the investment cost of new platforms. This is critical for health care providers, payers, pharmaceutical companies, and medical technology companies who need to deliver increased value to their patients at lower costs.

The integrated cloud approach allows multiple organizations to safely share common infrastructure, which reduces capital investment. Additionally, this approach naturally leads to the standardization of the surrounding IT processes used to manage the infrastructure.

Build consortiums. With the vast number of health care industry stakeholders, types of data, and loosely defined standards, it’s impossible for a single organization to build a widely accepted data platform. Medical technology firms can look to other industries for examples of integrated cloud consortiums. The Akisai cloud by Fujitsu⁴, for example, connects producers, consumers, wholesalers, retailers, and local governments and organizations for agricultural management.

Collaborate with regulators. Regulators have had a hard time defining strict safety and security requirements amid the proliferation of technology in the health care industry. The emergence of integrated cloud consortiums will bring regulators and corporations together to collaborate.

This will help regulators better define policies around mobile health, product security, and data privacy, thus improving patient safety and facilitating compliance innovation in the industry.

Focus on the customer. Anyone who has spent any time in a crowded doctor’s waiting room or wearing a flimsy hospital gown would agree that health care has remained immune to the increasing customer centricity permeating other industries.

Hospital executives have historically prioritized efficiency and productivity over customer experience. But as consumers demand a greater role in health care, companies are adjusting their approach. The focus on patient experience is becoming a differentiator and must be taken into account when developing new platforms.

1. Google AI Shows Promise

Company has developed similar AI systems for lung cancer, eye disease and kidney injury

Brianna Abbott

Jan. 1, 2020 1:00 pm ET

Google’s health research unit said it has developed an artificial-intelligence system that can match or outperform radiologists at detecting breast cancer, according to new research. But doctors still beat the machines in some cases.

The model, developed by an international team of researchers, caught cancers that were originally missed and reduced false-positive cancer flags for patients who didn’t actually have cancer, according to a paper published on Wednesday in the journal Nature. Data from thousands of mammograms from women in the U.K. and the U.S. was used to train the AI system.

But the algorithm isn’t yet ready for clinical use, the researchers said.

The model is the latest step in Google’s push into health care. The Alphabet Inc. GOOG -3.51% company has developed similar systems to detect lung cancer, eye disease and kidney injury.

Google and Alphabet have come under scrutiny for privacy concerns related to the use of patient data. A deal with Ascension, the second-largest health system in the U.S., allows Google to use AI to mine personal, identifiable health information from millions of patients to improve processes and care.

The health data used in the breast-cancer project doesn’t include identifiable information, Google Health officials said, and the data was stripped of personal indicators before being given to Google.

Radiologists and AI specialists said the model is promising, and officials at Google Health said the system could eventually support radiologists in improving breast-cancer detection and outcomes, as well as efficiency in mammogram reading.

“There’s enormous opportunity, not just in breast cancer but more widely, to use this type of technology to make screening more equitable and more accurate,” said Dominic King, the U.K. lead at Google Health. “It feels like this is another step towards this technology actually making a difference in the real world.”

Breast cancer is the second-leading cause of cancer death in women after lung cancer, and roughly one in eight women in the U.S. are likely to develop breast cancer throughout their lifetime, according to the American Cancer Society. Early breast-cancer detection and treatment can save lives, experts said, and most health systems have screening protocols.

But many cases of breast cancer are missed. And sometimes mammograms are flagged for women who don’t have breast cancer or whose cancer is generally harmless, leading to extra testing or unnecessary treatment.

“It’s this balance of finding the important cancers and not causing undue distress over false positives that aren’t going to hurt a woman,” said Emily Conant, a radiologist and division chief of breast imaging at Penn Medicine.

When developing the AI system from the U.K. dataset, researchers fed the algorithm mammograms from the U.K. National Health Service’s breast-screening program. The U.S. dataset comprised mammograms taken from Northwestern Memorial Hospital in Chicago. Whether a woman had breast cancer was previously determined, and researchers told the algorithm which cases had confirmed breast cancer.

The AI system was then tested on different mammograms of more than 25,000 women in the U.K. and 3,000 women in the U.S. from those datasets. The AI system reduced missed cases by 9.4% in the U.S. and 2.7% in the U.K. compared with the original radiologist diagnoses. It also reduced incorrect positive readings by 5.7% and 1.2%, respectively.

In the U.K., where two radiologists typically read a mammogram, the study found that the model didn’t perform worse than the second reader and could potentially reduce their workload by 88%.

The researchers then had six U.S. radiologists who didn’t make the original diagnoses look at 500 U.S. mammograms and compared their responses with the AI system’s. The radiologists also received the patients’ history and past mammograms when available, while the AI system didn’t. The AI system outperformed the average radiologist in determining whether the women would develop breast cancer.

While the AI system caught cancers that the radiologists missed, the radiologists in both the U.K. and the U.S. caught cancers that the AI system missed. Sometimes, all six U.S. readers caught a cancer that slipped past the AI, and vice versa, said Mozziyar Etemadi, a research assistant professor in anesthesiology and biomedical engineering at Northwestern University and a co-author of the paper.

The cancers that the AI system caught were generally more invasive than those caught by the radiologists; the researchers didn’t have an explanation for the discrepancies.

“I found it sobering,” said Ziad Obermeyer, acting associate professor of health policy and management at the University of California, Berkeley who studies machine learning and health and wasn’t involved in the research. “I think this is a testament to how difficult the task is and how weirdly good humans are at it, even with some of the best data in the world.”

Researchers now want to see how the model would behave in the clinic.

“The real world is more complicated and potentially more diverse than the type of controlled research environment reported in this study,” Etta Pisano, chief research officer at the American College of Radiology, wrote in an editorial in the journal Nature about the paper.

Google Health said it is talking with health systems and research groups about how best to incorporate the AI system into clinical workflow.

MBA 673 Info Systems in Healthcare

RUBRIC
Excellent Quality 95-100%	Introduction 45-41 points The background and significance of the problem and a clear statement of the research purpose is provided. The search history is mentioned.		Literature Support 91-84  points The background and significance of the problem and a clear statement of the research purpose is provided. The search history is mentioned.			Methodology 58-53 points Content is well-organized with headings for each slide and bulleted lists to group related material as needed. Use of font, color, graphics, effects, etc. to enhance readability and presentation content is excellent. Length requirements of 10 slides/pages or less is met.
Average Score 50-85%	40-38 points More depth/detail for the background and significance is needed, or the research detail is not clear. No search history information is provided.		83-76  points Review of relevant theoretical literature is evident, but there is little integration of studies into concepts related to problem. Review is partially focused and organized. Supporting and opposing research are included. Summary of information presented is included. Conclusion may not contain a biblical integration.			52-49  points Content is somewhat organized, but no structure is apparent. The use of font, color, graphics, effects, etc. is occasionally detracting to the presentation content. Length requirements may not be met.
Poor Quality 0-45%	37-1 points The background and/or significance are missing. No search history information is provided.		75-1 points Review of relevant theoretical literature is evident, but there is no integration of studies into concepts related to problem. Review is partially focused and organized. Supporting and opposing research are not included in the summary of information presented. Conclusion does not contain a biblical integration.			48-1 points There is no clear or logical organizational structure. No logical sequence is apparent. The use of font, color, graphics, effects etc. is often detracting to the presentation content. Length requirements may not be met
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