Except there's one thing: Ellie is not a person. She is a virtual character created by the Institute of Creative Technologies (ICT) through the University of Southern California (USC). Using a Microsoft Kinect, a video camera, and a microphone, Ellie is able to pick up activity levels and stress responses through body language, facial expressions, and voice cues, and is able to adjust questions accordingly. Ellie’s goal is to track potential signs of depression, which can be a symptom of a variety of mental disorders – something that the system is able to catch 90% of the time.

But Ellie comes with a lot of questions, both ethical and practical.

“A lot of what I do in my work is [address] why would you want a virtual person,” said Jonathan Gratch, a research associate professor of computer science and psychology at USC and associate director of virtual human research at ITC. “What are its advantages and what does it need to do?”

Funded through the Defense Advanced Research Projects Agency through the Department of Defense, ICT’s mission as a research institution has been to create virtual training software to be used for a variety of military requirements, such as interviewing and negotiating, before putting soldiers with actual people. Ellie was one of the developments that came from the initiative, but her mission would be very different. Instead of training, the focus would be on diagnosis and treatment.

The machines that monitor nonverbal communication are paired with some of the technology that was prevalent in ELIZA, or a chatbot from the 1960s that looked for keywords when talking to patients to map out a response. Gratch, who is overseeing the data collection and evaluation aspects of Ellie, said that the project is one step up from that, using certain behaviors and vocal cues to determine the next question. Unlike ELIZA, Ellie is programmed to have clinical protocol and not ask as many open-ended questions. The nonverbal element is also vital, as a person looking away or a pitch contour can often say more than just the words themselves.

Ellie was based off of an experienced clinician, which helped create a lot of the mannerisms that the program uses. There is backtracking, or sounds of acknowledgment, that a normal therapist would make to help connect. Gratch says that the rapport that Ellie builds, such as leaning in when a person is trying to lean back or smiling or nodding at key points, helps the patient feel more connected and willing to divulge information.

Ellie, being a virtual program, may have certain advantages that a human may not have in tracking conditions. Whereas a patient’s facial expressions and body language may be forgotten by a human psychotherapist, Ellie is capturing everything and analyzing it, comparing it to other patients and producing data. Also, a patient may be more comfortable disclosing behaviors to a computer program that they may not want to tell to an actual person but is vital information, such as risky sex and illegal drugs. It could be one variation of telemedicine.

However, the idea that Ellie would replace an actual human psychotherapist is a far-off concept, according to Gratch. He says the technology hasn’t advanced to that point, and even if it was, it leads to a variety of questions. These questions include whether using Ellie over a long period is the best use of resources or if it’s ethical to keep using her over an extended period. Rather, Gratch sees Ellie more as an augmentation of what people can do to treat patients in mental healthcare.

“It’s a better version of a self-help book, which is one way that people who are resistant to care seek care,” he says. He adds that he feels that it is bridging a gap that may exist in human care, not replacing it. And Gratch adds that Ellie is not able to give an exact diagnosis; rather, it’s a determination. He compares it to tracking a fever, but not understanding the cause of it. Depression, like a fever, can be co-present with many different mental conditions.

Gratch sees potential for Ellie beyond depression, such as helping in autism research, social disorders, and elder care. Also, he points out there is interest in seeing how a virtual character like Ellie can detect pain.

But until then, there is still data that needs to be collected. Gratch is hoping to put their technology in a kiosk in the Veterans Administration in order to collect in the field. Also, an independent group at the University of California, Los Angeles, will be doing individual research to help improve Ellie. And the program is still working to develop other characters similar to Ellie, in a different gender as well as various races, who could help make patients more comfortable. The hope is to eventually use this technology to help soldiers and track behavior changes pre- and post-deployment.

Reina V. Slutske is the assistant editor for MD+DI.

Related Links:

Inside Look—Startup Sees Need for Autism, Mental Illness Devices
 

How Much Are Universities Willing to Pay for Top Tier Medical Research Talent?
 

XBox Kinect: The Medical Device in Your Living Room?

 

As wireless medical devices move from the test bench to the hospital bed, product designers have turned their attention to solving the challenge of powering these technologies as well as reducing their overall need for power. Low-power draw medical body area network technologies, like ZigBee and Bluetooth 4.0 Low Energy, have reduced electric requirements to a point where these devices do not require large batteries or power cords. Advances in on-body power generation, flexible battery technology and battery-integrated devices are solving the power problem for wireless medical devices.
 

• Sol Chip and Cellergy have developed a solar-energy harvesting sensor that can be used in an array of devices.

  Solar capacitors. Two Israeli companies, Sol Chip and Cellergy, have developed a power source that uses a small solar cell to charge a super capacitor. Capacitors are electronic components that work similarly to batteries but with the ability to charge substantially faster than normal batteries. For example, leaving the window shades open for a few minutes could power a body-mounted sensor for an entire day.
   
• Glucose fuel cells. Small fuel cells developed by researchers at the Massachusetts Institute of Technology can use the body's glucose to generate electricity. These fuel cells are small enough to be used in implanted wireless medical devices and can draw small quantities of glucose from cerebrospinal fluid or other bodily secretions. Given that the body transfers energy in the form of glucose, it's a readily available fuel.
   
• Edible sodium-ion batteries. While small sodium-ion batteries may not have long life, they are an inexpensive solution for sensors that are designed to be swallowed. This is an already extant technology that is safe and non-toxic and can provide enough power to drive a sensor for a trip through the digestive system.
   
• Flexible batteries. The lithium-ion batteries that power cell phones, notebook computers and some power tools offer excellent density and efficiency but they remain inflexible. Researchers hope to expand the applications of such batteries by creating flexible battery cells that can be sewn into clothing or comfortably placed on the body. They could offer large enough quantities of power to serve an entire body area network of wireless medical devices. Other uses of flexible battery technology are in their use in conjunction with stick-on-tattoo-like sensors that are applied to the skin and provide their own power, data collection and wireless transmission service.
   
• Heat capture. The heat that a 98.6 degree human body emits can be harvested and used as a source of energy. An Oregon-based company known as Perpetua Power Source Technologies has invented a chip that converts body heat into electricity. It can be implanted in wireless medical devices to provide an always-on body source, as long as its wearer isn't hypothermic.

These technologies are just a few of the many on-body power generation and delivery systems being developed. A recent post on six promising technologies from MPMN Medtech Pulse blog covers more on-body power solutions that are in development.

At TED this year, Myshkin Ingawale of Biosense Technologies, Mumbai, India, announced the launch of an app that lets you test your urine. At the time, the product, called uChek, drew laughs from the audience as inevitable jokes of peeing on your phone riffled through the audience.

But the app is of course smarter, and more valuable than that. 

At the time, Brad Thompson wrote an editorial for MD+DI, asking whether the app was really a medical device. 
At that point, Biosense seemd to be trying to avoid medical device status, based on the company’s press materials, which stated that uChek is not intended to treat, cure, mitigate, or prevent disease.

“Everyone in the medical device industry probably recognizes that the company is trying to distinguish its intended use from one meeting the FDA’s medical device definition,” noted Thompson in his editorial.

Still, the function of uChek is urinalysis. Users pee on a test strip and place it on a color-coded mat. Then they take a photo with the camera’s phone. The app analyzes the strip colors against the swatches on the mat, and returns data about glucose, protein, and ketone levels in the sample. The user can save readings and track data over time Further, the company’s Web site states that “Using a urine dipstick along with uChek can help you detect presence of up to 25 diseases diabetes, pre-clampsia, urinary tract infection, etc. to name a few.”

For anyone involved in medical devices, this language and the process of the test is an undoubted signal that uChek is a medical device. But again, should it be a 510(k) submission, or is it a Class I device?

FDA is asking. Last week, the agency sent a rather polite “It has come to our attention,” letter to Ingawale requesting that the company supply either the FDA clearance number or an explanation as to why the company does not feel it needs clearance:

“Since your app allows a mobile phone to analyze the dipsticks, the phone and device as a whole functions as an automated strip reader. When these dipsticks are read by an automated strip reader, the dipsticks require new clearance as part of the test system. Therefore, any company intending to promote their device for use in analyzing, reading, and/or interpreting these dipsticks need to obtain clearance for the entire urinalysis test system (i.e., the strip reader and the test strips, as used together).

Brad Thompson again weighed in, noting that he had played a role in getting FDA to send this letter. Thompson’s reasoning for getting involved is to ensure continued clarity in regards to FDA’s practices for regulating mobile apps. As he said, “I think FDA simply decided to respond to all of the public questions around whether apps trigger FDA requirements if they perform the same functions as traditional, regulated medical devices.”

Thompson also noted that watching what FDA does is a key way that industry can draw conclusions on how FDA plans to regulate. From his perspective, FDA’s lack of action causes more confusion than its actions, particularly with health app regulations.

Biosense’s believes it is acting responsibly. The company, according to an e-mail I received from Myshkin Ingawale, is convinced the product is an "automated urinalysis stystem," per FDA's definition. In April, the company recieved Class I status.

On May 30th, the firm released a note to its mailing list that calls attention to the regulatory issues at hand. Within the text of the e-mail, the company says:

“Also interesting development: uChek is a FDA registered device and we have been in discussions with the regulator about the classification of the product. Much commentary about this on the web. Like here.  We seem to be somehow in the center of a much wider debate about innovation and regulation.

Thompson sent me an e-mail about this campaign, calling the approach rather arrogant. “They go on a promotional campaign to encourage more downloads. Wow. Further, in the email they emphasize the two uses that trigger the FDA clearance requirement: testing glucose for diabetes and testing for occult blood.”

Thompson’s response to the e-mail is critical to Biosense's response. And rather than rework his comments, I'll include some key passages (edited for style): 

“They note that they received a letter from FDA and that there are some ongoing discussions about classification. That's putting it pretty mildly. FDA said they need prior clearance before selling their product in the US and the agency couldn't find any clearance on record. . . . The law is clear on the need for FDA clearance prior to marketing.  This isn't some interesting policy debate. The company is making a medical device that is used to diagnose and manage important diseases like diabetes and liver disease.

While the agency normally handles much of the enforcement work they do privately, my guess is we will see at some point a more public resolution to this. If the company persists in arrogantly disregarding FDA's authority, I suspect we will see FDA act decisively.

The company’s defense to public criticism that it has in fact registered its facility to produce Class I medical devices should not be given any weight for the following reasons:

1. Most importantly, registration has absolutely nothing to do with the issue that FDA has raised, which is the fact that the company needs to get FDA clearance before going to market. Getting clearance can be a substantial undertaking and it would appear that the company has not done the type of clinical research that would be necessary to support clearance.
2. The fact that they've done something-- one thing-- FDA requires, hardly seems relevant or for that matter impressive. I assume that they also sent their mothers’ cards on Mother's Day, but that hardly seems a defense to failing to seek FDA clearance before marketing.
3. Just so people understand what getting FDA registration means; that means they spent literally at most a half-hour on the agency's website typing in basic information about their location.
4. Further, they only did that because of the expressions of concern in the media back in March about the company's compliance. Up to that point, on the company's website they had declared that the app was not a medical device and not required to comply with FDA requirements. It was only when stories started to appear in the media that the company capitulated and agreed its product was a medical device of any kind.
5. I can only speculate whether the company has truly complied with the more important requirements such as the quality system. As of March the company was declaring that its product was not a medical device; a few weeks later the company was finally agreeing that its product was a medical device. Are we to believe that in that time the company fully installed an FDA compliant quality system? Among other things, a quality system would require that the software be developed using design controls. Frankly it's really difficult to go back in time once a product is complete and somehow retroactively comply with the design controls. How on earth did they do that in such short order?

So if the company expects some sort of applause by finally—under pressure—spending a half hour on the FDA Web site to register, I'm afraid I'm not impressed.

—Brad Thompson

To codify Thompson's point, there is a big difference between uChek's actions of registering as a Class I device, and responding to FDA's letter to explain why the device doesn't have "clearance," which signifys a 510(k) Class II submission.

According to Ingawale "We are not engaed(or even smart enough politically, to engae) in any PR campaign." He says, "we are a small company, merely trying to provide accurate, convenient diagnostics to all users."

Ingawale notes Biosense's willingness to work with FDA, and says he is "pained to see that uChek is being protrayed as some sort of pantomime villain here. We have been following the rules right from the begining." 

It will be interesting to see how this saga plays out. As mentioned, how FDA responds and acts from here on out should be incredibly informative for the rest of the mobile device world. On a related note, Scanadu is developing a similar app called ScanFlo, but that company is waiting for FDA approval. 

—Heather Thompson is editor-in-chief of MD+DI.

Related Content

Looking for the Next Mobile Health Hub? How about Africa? 

Wanted: FDA App Enforcement

Scanadu Scout Crowdfunds the Tricorder Out of Darkness

Note: This article is based on the white paper "Establishing Bioburden Alert and Action Levels" available for download.

Most national and international standards regarding bioburden, sterilization, or environmental testing recommend establishing alert and action levels to demonstrate continued control over a process or product.

ISO 11737-1:2006 provides guidance on establishing bioburden alert and action levels.¹ Clauses A.8.5–8.7 give general guidelines for setting environmental or bioburden levels. This guideline does not dictate how to use the data to establish action and alert levels, nor does it provide guidance on how to interpret the data depending on the sterilization modality in use.

The standard for radiation sterilization, ISO 11137-2:2006, assumes that dose audits are being performed quarterly. Clause 10.1 states

A review of environmental and manufacturing controls, together with determinations of bioburden should be conducted in conjunction with sterilization dose audits. If the review indicates lack of control, appropriate action should be taken.

No definition is provided for the phrase “indicates lack of control.” It is clear that some criteria should be established. Most companies comply with this requirement by establishing alert and action levels for bioburden and environmental counts.

There are many factors involved in establishing bioburden alert and action levels in a variety of situations.

Normal Distribution

The 11737-1 document discusses the fact that bioburden data seldom fit into a normal distribution (i.e., a bell-shaped curve). In evaluating bioburden data consider whether it is important that the data fit a standard statistical model (e.g., normal distribution). That the data fit a standard statistical model is less critical than whether the established levels are based on empirical data and whether they provide safety from a sterilization perspective.

One primary reason that bioburden data do not fit a normal distribution is due to bioburden spikes. It is common to obtain most bioburden values near the mean but also to occasionally have a value that is well above the mean (i.e., a bioburden spike). Bioburden spikes are common in the medical device industry, especially with manual assembly.

The other main reason for bioburden data not fitting a normal distribution is because of the frequent occurrence of zero colony forming units (CFUs) results (e.g., <1 CFU per sample). Standard distribution in this case may be zero; thus, use of standard distributions is impossible, and a different approach is required.

Alert and Action Levels

For alerts and actions, some use the term limits rather than levels. The term limit implies that a product has been effected by an excursion above that value. Use of levels does not imply that the product has automatically been impacted and is generally preferred. A search into established documents and standards provides definitions regarding alert and action levels (or limits). ^2–5

Alert Level. Indicates when a process might have drifted from normal operating conditions. An investigation may be performed and corrective action may be implemented, but no action is required. It can be assumed that repetitive excursions above the alert level may be addressed as if it were an action level.

Action Level. Indicates that a process has drifted from normal operating conditions. An investigation must be performed and corrective action must be implemented.

Manufacturers are responsible for setting their own internal specifications for bioburden and environmental alert and action levels. Alert and action levels should be used as a means to monitor manufacturing processes and not as stand-alone product acceptance criteria.

Neither alert nor action levels should be based solely on environmental or bioburden counts without considering the method of sterilization and the amount of overkill in the cycle. In setting the levels there should be a balance between demonstrating adequate control over bioburden without frequently triggering the alert and action levels.
Setting levels is not purely a mathematical exercise. It also involves looking at the proposed levels with common sense.

TNTCs, Spreaders, and Spikes

A bioburden or environmental agar plate may have growth covering the entire surface where distinct colonies cannot be enumerated. These are usually called too numerous to count (TNTC) or spreaders. TNTC describes individual colonies that are indistinguishable because of high numbers of colonies on the filter or plate. Spreaders describe one or more colonies that have covered a portion of or the entire filter or plate. Spreading can be caused by particulates, the nature of the microorganisms, or by fluid on the filter or plate.

TNTC results should not be assigned a CFU value. Using an assigned value beyond the countable range, such as 300, would likely result in an underestimation of the bioburden. In review of any bioburden data, a TNTC result likely indicates a bioburden problem and signals an investigation. The investigation may call for additional testing.

Spreaders do not allow for an accurate count. The count should be discarded when gathering historical data to establish bioburden levels. Spreaders generally indicate a problem with the test method.

Occasionally, spikes are observed in bioburden testing. Currently there is no harmonized definition for a bioburden spike. One common definition is an individual value that is greater than or equal to twice the mean.

Spikes or outliers should be investigated. If they are not true values, then either that value or the entire data set should be discarded. If the investigation determines that they are true values, there may be a bioburden problem in the manufacturing or testing process. It is unwise to set alert and action levels while such a problem is present. If possible, the cause of the spike should be identified and corrected. If this is not done, infrequent spikes could eventually become more frequent.

As part of the investigation, determine whether the spike value raises a potential concern regarding the ability of the current sterilization cycle to provide product that is sterile to the desired sterility assurance level (SAL). This evaluation varies depending on the sterilization mode used and on the bioburden counts at the time of the validation (see Table I).

Table I. Notice the spike in sample 4 in this example of bioburden data.

Each product or product family should be evaluated and established independently, based on historical trends.

Initial Levels

When establishing levels for a new product, use initial or temporary levels until enough data are gathered to establish long-term levels.

Initially test the samples more frequently (e.g., weekly or monthly) to establish a baseline. With these baseline data, temporary alert and action levels can be established. Testing on a typical basis (e.g., quarterly) for the remainder of the year will result in sufficient data for determining long-term alert and action levels.

Three initial sets of data representing three batches can provide a good statistical basis for temporary levels. Use of the same mathematical approaches for establishing temporary versus long-term levels is appropriate with the understanding that the temporary levels may be triggered more frequently.

Create a plan for setting long-term alert and action levels. It should cover the transition of temporary to long-term levels and the frequency of reevaluation.

Long-Term Levels

Once sufficient bioburden data have been gathered, long-term alert and action levels should be established. When gathering data, consider the following to ensure that sufficient data representative of the product have been gathered:

Samples should represent the entire lot. If a manufacturing batch is made specific for testing, extra care must be taken to ensure that the testing batch is representative of routine manufacturing. ²

Bioburden data should be gathered over an extended period of time. It is typical to gather data over one year. ²

At least four sets of data should be used. As more data are gathered, the margin of error decreases. For example, one set of 10 samples per quarter of the year (40 data points) generally provides sufficient trending to establish levels.

Employ a validated recovery efficiency for product bioburden levels. A recovery efficiency validation should be performed for each sample product type (e.g., minimum of three samples) and applied to all data points before data evaluation begins. If multiple recovery efficiencies are determined over time, take the mean of all recovery efficiencies and add them to each set of data. Applying the same recovery efficiency to all data provides for less variation when comparing bioburden estimates and is applicable as long as the same extraction method is used for each set of data. In the bioburden standard, derive the correction factor from the recovery efficiency.

Using standard deviations to set levels is a simple and easy approach. A misleading argument against using standard deviations is that microbiological data may not fit a normal distribution. However, the standard deviation is a useful measure of the dispersion of the data, even if data are not normally distributed.

As a larger sample size of bioburden data becomes available, a move toward a normal distribution may not always be seen. Although a larger sample size could result in a normal distribution of microbiological data, the presence of even a single very high value could result in the data not being normally distributed.

Additionally, a larger sample size of bioburden may not necessarily move toward a normal distribution if there is no growth (e.g., 0 CFU observed). In this situation, the sterilization method may be used to establish the alert and action levels. Another option is to use other distributions and their corresponding statistics to establish levels. Although low bioburden data are said to follow a Poisson distribution, in our evaluation of 47 data sets of product with high bioburden, the Poisson distribution was generally not found.

It is not desirable that the alert level be triggered often, as that would be an indication that there is either too much variability in the bioburden results or that the alert level is too low.

It is best to use the bioburden estimate to establish values rather than bioburden averages or maximum values. This would require that a recovery efficiency be validated for each product type to calculate the bioburden estimate. For environmental monitoring, the bioburden average would be used because a recovery efficiency is generally not performed.

Table IIa: This table shows bioburden data-monthly monitoring. Three initial sets of data representing three batches can provide a good statistical basis for temporary levels.

Table IIb. From a bioburden perspective, a comparison of the first three months (See table IIa) versus the entire year shows the bioburden estimate and bioburden estimate plus standard deviations are similar. This demonstrates that, as the manufacturing process was refined over time, there was not a significant change and the bioburden is similar.

Initial Evaluation of the Data

Tests are usually performed monthly for the first quarter, then quarterly for the rest of the year. Using bioburden data from the product in question, the mean, standard deviation, and bioburden estimate for each set can be calculated as well as the overall mean, average standard deviation, and average bioburden estimate. The sum of aerobic bacterial and fungal data for each sample could be used in all calculations.

Additional calculations were performed to determine the bioburden estimate plus two and three standard deviations as well as the bioburden estimate times 10 (see Table II, parts a,b, and c).

From a bioburden perspective, a comparison of the first three months versus the entire year might show that the bioburden estimate and bioburden estimate plus standard deviations are similar. This would demonstrate that, as the manufacturing process was refined over time, there was not a significant change and the bioburden is similar.

Using standard deviations to establish the bioburden levels is similar to the “normal distribution approach” in PDA TR13.2 The alert level can be set at two standard deviations above the historical bioburden estimate, and the action level can be set at three standard deviations above the historical bioburden estimate. This approach results in tight alert and action levels, which would be appropriate for bioburden-based methods such as radiation.

Table IIc. The established recovery efficiency of 58.7% was applied to all data for consistency.  This is appropriate, because all testing was performed using the same extraction method.

For radiation sterilization using VDmax, there is an established bioburden count that should not be exceeded, which is the maximum bioburden count permitted in the sterilization table being used in ISO 11137-2 and ISO 13004.1.^1,6 For example, for 25 kGy, the maximum allowable bioburden count is 1000 CFUs. This would be an example of when the term limit might be appropriate.

When establishing levels for overkill-based methods (e.g., EtO), alert and action levels could be based using the bioburden estimate + 3 × standard deviations and bioburden estimate × 10, respectively. A good limit for such products using overkill methods could be when the bioburden approaches or exceeds the titer of the biological indicator. The amount of safety provided in overkill cycles should allow for greater flexibility in the alert and action levels.

Evaluation of Data Normality

The statistical analysis system (SAS) PROC UNIVARIATE was used to evaluate the normality of 47 different data sets (10 samples per data set). The following four different statistical tests were used in these evaluations: Shapiro-Wilk, Kolmogorov-Smirnov, Cramer-von Mises, and Anderson-Darling. Each of these tests did agree reasonably well on the determination of the normality of each of these data sets. Many data sets were found to be abnormal due to a single outlier, which was more than twice the standard deviation beyond the mean (33 out of 47, or 70%).

In the evaluation of these data sets, it was determined whether each data set had a single outlier, which was defined as a single data point more than twice the standard deviation beyond the mean (i.e., mean + 2 standard deviations). Most data sets were either abnormal due to an outlier or normal due to no outlier (39 out of 47 or 83% were deemed abnormal). This result demonstrates that using the SAS program may be a simple and reasonably accurate way to determine whether a given data set has an influential outlier.

When the initial sets of 10 were grouped into sample sizes of 20 or 30 data points (based on product type) and evaluated as described earlier, the data did not become normal solely based on a larger sample size. In fact, the rule of thumb described worked with each of the data sets in determining normality (100%).

Conclusion

There are many factors involved in establishing alert and action levels for product and environmental bioburden. A thorough review of bioburden data can assist in selecting the best approach for the situation. The approaches discussed here have functioned well for a variety of product and sterilization types.

There is often discussion in the industry regarding the appropriateness of standard distributions for evaluating bioburden data. Fitting the bioburden data into a specific statistical distribution is less critical than understanding the ranges of bioburden over time.

An important part of this process is having a good definition for alert and action levels and understanding what should occur when each is triggered. Different sterilization types should require different numerical levels as well as specified follow-up actions.

References

1. SO 11737-1:2006, “Sterilization of Medical Devices—Microbiological Methods—Part 1: Determination of a Population of Microorganisms on Products” (Geneva: International Organization for Standardization, 2006).
2. PDA Technical Report 13, “Fundamentals of an Environmental Monitoring Program” (Bethesda, MD: Parenteral Drug Association, 2001).
3. USP <1116>, “Microbiological Control and Monitoring of Aseptic Processing Environments” (Rockville, MD: United States Pharmacopoeial Convention, 2012)
4. USP <1231>, “Water for Pharmaceutical Purposes” (Rockville, MD: United States Pharmacopoeial Convention, 2012)
5. Code of Federal Regulations 21 CFR 211.
6. ISO 13004, “Sterilization of Healthcare Products— Radiation—Substantiation of Selected Sterilization Dose: Method VDmaxSD” (Geneva: International Organization for Standardization, July 2013 [not yet published]).

Martell Winters is a senior scientist at Nelson Laboratories, where he has worked for 18 years. He has been involved in writing AAMI/ISO and AATB documents for 15 years. Winters is a registered microbiologist and the specialist microbiologist. 
Esther Patch is study director II for Nelson Laboratories. She graduated with a degree in chemistry with an emphasis in biochemistry and a minor in biology from Erskine College and Seminary (Due West, SC). She is a national registered biologist. 
Wendy Wangsgard is bioburden department scientist and has been with Nelson Laboratories for eight years. She is involved with the radiation sterilization, microbiological methods, sterility assurance level, and other working groups of AAMI. 
Harry Bushar is an independent statistician currently employed part-time by FDA. He has served as a member of the AAMI Radiation Sterilization Subcommittee and the Gamma Radiation Sterilization Working Group. 
Ashley Ferry is a quality assurance investigator at Nelson Laboratories. Ferry also audits testing to ensure compliance with cGMPs, ISO/AAMI, USP, and internal SOPs and STPs. She is a registered microbiologist. 

Imagine caring for a small, frail patient like a premature child. In addition to having to watch out for IVs and feeding tubes, you'd have to navigate around pulse oximeters, blood pressure cuffs, EKG leads and additional monitoring equipment. Tasks as simple as changing a diaper or dressing the child become intricate processes requiring great care to avoid dislodging a sensor. Now, imagine what it would be like to care for that patient more directly--without any leads in your way and simply being able to attend to his or her needs. The new wireless medical device applications under development are making that hope a reality.

The Nonin 3230 pulse oximeter could be the first wireless pulse oximeter to use Bluetooth Low Energy technology. The device is awaiting 510(k) clearance in the United States.

The medical device industry is starting to witness a profound shift away from the cumbersome wire-based patient monitoring technology that has existed for decades. While wearable devices like the Holter monitor have helped to revolutionize patient monitoring, the spaghetti of leads and cables that accompanies them can be cumbersome for patients and caregivers alike. Now, wireless technology is enabling the development of devices that are more comfortable for patients that also collect patient data with unprecedented frequency and accuracy. Also contributing to this trend is FCC's allotment of spectrum for MBANs in 2012 was a significant milestone for the field of wireless patient monitoring.

[Join a discussion about wireless technologies at MDM East - Philip Raymond, Wireless Architect at Philips Healthcare and Ken Fuchs, Senior Principal Architect, MindRay, will share some ideas.]

Every week MD+DI curates content from all over the Web to share some of the most interesting articles, longreads, and videos with the medical device community.

The 15 Scariest Looking Medical Devices

For Warsaw, IN, mayor Joseph M. Thallemer, the orthopedic history of his city hits close to home – or rather, his practice. In 1977, while the four founders of Biomet were looking at hips, Thallemer was looking at their eyes.

“I was their optometrist,” he says with a laugh. He watched them from the beginning – from breaking ground on their facility, flailing and borrowing money from their parents to survive, and then eventually Biomet booming to be one of the biggest orthopedic firms in the world.

Warsaw has a lot of pride in the fact that they are known as the “Orthopedic Capital of the World,” and the industry and city are undisputedly tied together. “It’s been such a heritage in our community, and the industry has provided great economic foundation for the community for jobs and tax space,” Thallamer says. So how did a random town in Indiana gain such a lofty title?

When Revra DePuy began his namesake company in 1895 in the town to make wire mesh and wooden splints, it began a generation-to-generation culture of orthopedic business. In 1927, resident J.O. Zimmer, who was originally an employee of DePuy, spun off and founded his own company. With Biomet opening in the area in 1977 courtesy of four former Zimmer employees, it meant that three of the biggest orthopedic OEMs in the world were located in the Warsaw area.

These companies have led to tier one and two suppliers opening up locally, and also other large companies setting up satellite offices, such as Medtronic. The town has also become the center of orthopedic conventions, such as Orthotec 2013 June 4-6. These companies and events are both in the city and just outside the limits, and provide a lot to the community as a whole, particularly for work. According to Brad Bishop, executive director of the nonprofit Orthoworx, there are approximately 6800 orthopedic-industry related jobs in the Warsaw area, or 25 percent of workers.

Orthoworx was set up in 2009 to help keep the orthopedic companies in the community and be able to provide for them so they could provide for Warsaw. The biggest pushes have been in education and recruitment; getting residents ready for the jobs that the companies in their town need, and then making the community attractive and diverse enough to bring in the best talent.

Bishop pointed out two different educations programs that have implemented. One of them was through Grace College, a local private school, where a master’s degree program in regulatory and clinical affairs was set up, which Bishop says is very specific to the industry. The other was through Ivy Tech Community College, which provides certificates in orthopedic manufacturing specifically for the big three. Thallemer says the community college program was great for the big three.

“The companies pluck those folks [out of the program] and put them on the manufacturing floors,” Thallemer says.

Residents are also looking outside to increase diversity in their community to attract talent from all over the world, even creating a council. For Bishop, this is vital to bring in the best people possible. “Diversity comes natural in a place like Los Angeles, but doesn’t happen as automatically in a place like Indiana,” he says. “We want everyone to feel welcome in this community.”

Thallemer acknowledges that things have been difficult for the industry, with the recession and the recent medical device tax being only several of the factors that have impacted the town of Warsaw.

“There are external forces outside of our control chipping away in all industry,” he says. “It’s important that we realize and recognize that this isn’t something we take for granted.”

As the mayor, Thallemer meets with the CEOs of the big three firms once a year in order to take a temperature gauge. He says he’s had great conversations with them about the issues that they are facing. There have been some difficulties, but they all remain committed to the community where each of them have planted roots, whether it’s funding the local YMCA or providing jobs.

“It’s somewhat of a symbiotic relationship,” he says. “We want them to be successful, and they want the community to be successful so they can have a great working environment and a great living environment.”

 Reina V. Slutske is the assistant editor for MD+DI. 

Related Content:

Bone Grafting — Is There Another Way?

Andrew Ekdahl's Role in DePuy Orthopedics Hip Recall Litigation

Analyst: Zimmer's newly launched Persona knee is more evolutionary than revolutionary