Enigma Forensics are experts in collecting and understanding electronic medical records or the EMR audit trail. Check out this blog to view our list of EMR Discovery Questions.
Electronic Medical Records (EMR) can be tricky! In most cases, during eDiscovery, you get what you ask for and only what you ask for! Every Discovery request involving a healthcare provider has unique aspects that need to be considered.
Enigma Forensics is an established Computer Forensic Expert Witness firm that has been involved in many medical malpractice cases and specializes in interpreting electronic medical records (EMR) audit trail or audit logs. Our staff has extensive experience with numerous EMR applications and can assist you with navigating through the challenges of EMR Audit Trails and/or Audit Logs. Electronic Medical Record a.k.a., EMR audit trail or log is the answer to who knew what when, in essence, it tells the story about what took place during the treatment of that patient.
The following is a list of important questions to file for the demand for eDiscovery for Electronic Medical Records, in a medical malpractice case.
Provide the name of all medical software applications utilized to store [Patient Name]’s Electronic Medical Records (EMR).
For each medical software application that contains [Patient Name]’s EMR, please provide the specific version of the software as well as the name of the company that produces the software during the relevant time period beginning on [beginning date] through the present date.
For each medical software application that contains [Patient Name]’s EMR, please indicate if any of the specified software applications were migrated off to a new platform and what the current status is of [Patient Name]’s EMR on the original system.
For each medical software application that contains [Patient Name]’s EMR, please provide the application administrators that have full access to the stored data and audit trails.
For each medical software application that contains [Patient Name]’s EMR, please provide all user and administrator manuals for each of the medical software applications.
For each application that contains [Health Care Provider Name]’s EMR, please provide the current retention settings for the audit trail for all patient’s EMR. Are the privacy log retention settings sent to a secondary audit log (e.g., Fair Warning)? Is the secondary audit log retention configurable within the systems and/or applications?
For each application that contains [Health Care Provider Name]’s EMR, please provide the earliest date that [Patient Name]’s EMR appears in the application’s audit trail.
Please provide the complete EMR audit trail for [Patient Name] detailing any health care provider’s access, review, modification, printing, faxing, or deletion activities in a comma-delimited format with any and all corresponding native files that may relate to the Electronic Medical Record for [Patient Name] as required by the Health Insurance Portability and Accountability Act § 164.312(a)(1). Such an audit trail should include the original values and new values for any alteration of the EMR and shall indicate the user making the change and the date and time of the change.
Please provide the data dictionary for each software application containing [Patient Name]’s EMR. Such dictionary shall include the username key that maps the real names of individuals to their unique user login account IDs for each medical software application containing any EMR for [Patient Name] as required by the Health Insurance Portability and Accountability Act § 164.312(a)(2)(i). Additionally, any lab test, codes, or other short-form identifiers included in [Patient Name]’s EMR Chart or EMR audit trail should be provided as part of the data dictionary production.
Please provide any and all original voice transcription recordings that were made by [Health Care Provider Name], or any other staff that related to [Patient Name].
Please provide any other native electronic files or emails that relate to [Patient Name] in the native format with an index containing the original unmodified metadata for each of the native files or emails produced.
Please provide any DICOM files that were captured as part of [Patient Name]’s treatment by [Health Care Provider].
Please provide electronic records of any outbound faxes and/or other methods of communication that were utilized by [Health Care Provider Name] to [EMR Recipient], in its native form with a corresponding comma file listing containing all available metadata in a delimited format with the corresponding file path to the native file produced for each record.
Please provide the name and title of the person most knowledgeable for the [Health Care Provider Name]’s software/auditing and compliance system.
What customizations and settings were active at the time when the plaintiff was admitted into the hospital? What privacy-related logging is in place for each such system and/or application? Are privacy log retention settings in place for each such system and/or audit log?
ZyLAB is a global company that can help an organization who has to deal with various regulatory authorities spanning the globe. They are dual-headquartered in both Washington, D.C. as well as Amsterdam in the Netherlands. If your dealing with GDPR in the EU or CCPA in the US ZyLAB is equipped to provide service. In this video blog Lee Neubecker and ZyLAB’s Jeffrey Wolff discuss what differentiates them from their competitors.
Cyber Forensic Expert Lee Neubecker and ZyLAB’s eDiscovery Director Jeffrey Wolff discusses how ZyLAB Artificial Intelligence (AI) solutions can help your company. ZyLAB is an eDiscovery provider that works with government entities, corporations and law firms to provide data solutions. ZyLAB assists in extracting value from data, and not just metadata, but also document review that is about looking for entity information. ZyLAB is able to search for key people, places, and organizations that are mentioned in documents and/or emails, and quickly drill down to what is going on in your organization.
Watch this important final part of our 3-Part Series on Artificial Intelligence Solutions and eDiscovery. You will learn about what ZyLAB offers that will help your company with document review and ultimately save time and money.
Part 3 of our 3-Part Series Artificial Intelligence (AI) solutions and eDiscovery
The Video Transcript Follows.
Lee Neubecker (LN): Hi, I have Jeff Wolff, back on the show from ZyLAB. Jeff, thanks for coming back on.
Jeff Wolff (JW): Thank you.
LN: He’s their Director of eDiscovery, and I wanted to ask him some questions as it related to what differentiates ZyLAB from other products out on the market. Some of my clients may want to use this type of artificial intelligence program to help get through their review and see what the results are of using AI versus the traditional e-discovery review process, so.
JW: Sure.
LN: Jeff, could you tell us what sets ZyLAB apart from other competitors in the marketplace.
JW: Sure, sure, so first, I think ZyLAB is uniquely positioned in the fact we understand the corporate space quite well, as well as the law firm space, but we got our start in the corporate world, or in information governance. So we are very vested in search and data science, and that’s really where we’ve put a lot of our focus. We have both on-premise solutions, as well as cloud-based, SaaS solutions like every other next-gen provider. But we really push our interface, our user interface and our user experience, as one of the most unique selling points. And that is, that it is not difficult to start using. Anyone, any legal professional can pick up our product in an hour, from start to finish, and understand really how you utilize it. Drag and drop interfaces for getting data into the system, and immediate color-coding and tagging, easy search, and the ability to really visualize your data and understand what’s in the dataset.
LN: Okay. So, what would you say for a company that has to deal with multiple jurisdictions, they’re in Europe, they’re in the US.
JW: Sure.
LN: There are some unique challenges posed by all the various regulations out there, like GDPR.
JW: Right.
LN: Maybe the have operations in China. How could you help a company that has to deal with various regulatory authorities spanning the globe?
JW: Sure, and that’s another advantage that ZyLAB has, actually, we’re actually a global company, so we’re dual-headquartered in Washington, D.C., here in the US, as well as Amsterdam in the Netherlands, in the EU. And as a result, we have cloud operations in both jurisdictions. So our global customers can actually keep US data in the US, and they can keep European Union in the EU, and not worry about that issue. But we also have the expertise, consulting expertise, in both environments, both geographic locations. For example, I’m doing a lot of work now with corporations, not so much focused on directly just on e-discovery, because e-discovery is a bit reactive, you know? Or corporations go through peaks and valleys with e-discovery, the litigation, something they have it, sometimes they don’t. What they constantly have though, are internal investigations, regulatory responses, in the highly regulated corporations. And more and more now, data privacy concerns. So, my European colleagues have been dealing with GDPR for a while, we’re now starting to feel it here in the US, with CCPA, the California Consumer Privacy Act. And there are a number of states on the horizon that are going to California’s examples, so corporations need to be able to find, and classify all the data that they have in their organization that has customer information because if those customers request it and they can’t provide it, they’re financially in a lot of trouble.
LN: Do you think that the regulations coming down on companies are going to fundamentally change how companies chose to communicate with their vendors, suppliers, and own employees?
JW: Absolutely. If you look at all the recent data breach situations, it’s typically not the organization that has the problem, and I won’t mention any of the large companies that have recently had data breaches, but it’s typically not the original company that had the issue, it’s one of their suppliers, or one of their vendors that had accesses to the database, and wasn’t protecting it properly, and that’s how the trouble began.
LN: Yeah.
JW: Same thing with data privacy.
LN: The supply chain certainly is a huge point of vulnerability for all types of organizations. The governments, the military.
JW: Yep.
LN: and even corporations.
JW: Yes.
LN: So what do you see happening over the next few years with the adoption of AI platforms?
JW: I think the e-discovery market is going to fundamentally change. There’s still always going to be a need for discovery within corporations and law firms, but what you do you with the data is going to become much more important, so it’s going to be about how you can extract value from the data, not just metadata, which we’ve always been able to do for years now, but now more about looking for entity information. People, place, organizations that are mentioned in documents and emails, and collaborative environments, and being able to visualize those, and quickly drill down to what was going on in your organization. You know, if you got people that are going to the dentist three times a week, they’re not doing to the dentist, they’re doing something else, They’re just writing about going to the dentist.
LN: Yeah.
JW: Software like ours that can identify those references in documents are going to be crucial to the success of organizations.
LN: That’s great. So it seems that there’s continued e-discovery service provider consolidation out there.
JW: Mhmm.
LN: The companies that are using tools that are more of a channel partner tool to resell.
JW: Yes.
LN: But as those companies consolidate, do you think that there’s going to be a movement away from those providers where, the company, the firms, directly do their own e-discovery?
JW: Oh, yes. Yeah, very much so. We’ve been seeing that over the last few years. A lot of companies, even small companies that tend to have, in the past, just used outside vendors for e-discovery, are now deciding that they prefer to control, not just the cost, but also their data. They don’t want their data outside of the organization for reasons we’ve already talked about. So they’re purchasing in-house tools that they can use themselves, and then they can invite outside counsel in to make use of, that way they control their costs, they control the efficiency, and they control the data.
LN: Well, this has been great. Thanks a bunch for being on the show.
JW: Thank you again.
LN: Take care.
JW: Bye-bye.
Part 1 of our 3-Part Series on Artificial Intelligence
Artificial Intelligence (AI) is the fastest-growing eDiscovery solution in the Legal Industry. Just like in Henry Ford’s day, it’s the keen cutting edge shaving away costs by reducing time spent from evidence to production. Use AI and don’t land in the pitfall.
“Competition is the keen cutting edge of business, always shaving away at costs”…Henry Ford
Is there a pitfall if you use AI? Computer Forensic Experts Lee Neubecker interviews Chief Innovation Office with DISCO, Cat Casey both agree the largest pitfall in AI is NOT embracing AI! Artificial Intelligence (AI) is the fastest-growing eDiscovery solution in the Legal Industry. Just like in Henry Ford’s day, it’s the keen cutting edge shaving away costs by reducing time spent from evidence to production.
Cat explains DISCO was born out of the firm’s frustration with conventional eDiscovery tools that were slow and difficult for lawyers to use. Instead of being forced to adapt our work methods to technology, we wanted to invent technology that works the way lawyers work. DISCO was the result, and today we are the fastest-growing eDiscovery solution in North America. Both experts agree implementing AI will help companies gain a competitive edge. Watch this video to hear examples of how AI helps sharpen that edge!
Final Part of our 3-Part Series in Artificial Intelligence: Pitfalls in AI
The Video Transcript Pitfalls in AI Follows.
Lee Neubecker (LN): Hi and welcome back again Cat. Thanks for being on the show again.
Cat Casey (CC): My pleasure.
LN: Cat Casey from CS Disco. She’s a Chief Product Innovation Officer. Did I say that right?
CC: Chief Innovation Officer.
LN: Okay.
CC: Products too, though. It’s fine.
LN: They call her chief.
CC: They should.
LN: So we’re going to talk now, in this last part of our series on artificial intelligence, about some of the challenges of organizations that don’t adapt and don’t get on board. So, what do you see the potential risks and pitfalls for law firms that don’t begin to embrace so sort the form of a technology-assisted review or artificial intelligence to help speed up the review process?
CC: Well, at a very basic level, clients are getting smarter. We’ve got CLOC https://cloc.org/, we’ve got clients talking to each other more, and they’ve raised their expectations of how their firms are going to be competitive. And it used to be if you were big law firm A you would always have this corporate client for every anti-trust case they would always go to you. But now I was getting dozens of RFPs where they’re asking me what technology are you using? How are you driving innovation? How are you driving efficiency? Because there is a higher expectation of competition between outside counsel. That, maybe, wasn’t there a few years ago. And so, the client expectation is driving this appetite to investigate eDiscovery and Artificial Innovation (AI) based innovation in a way that wasn’t here a few years ago.
LN: Has there been any industry research that has attempted to benchmark the cost of a case using an AI platform to speed up review versus not, to your knowledge?
CC: You know. I can speak from Disco, and we see about a 60% reduction in time to evidence to production. And that translated to dollars. And so, I mean, 60% savings on the 80% of a case that is reviewed is substantial. The thing that I think is most important is cost-savings big, but getting evidence quicker.
LN: Yeah. Time is of the essence.
CC: That is the thing that is paramount because of a lot of these companies… I worked at a company that had very big budgets, but no amount of money, no amount of people, was going to be enough to get these insights I needed before the meet and confer. Or before I had a critical filing with a government investigator. And so, getting evidence quicker so I can start building my case, was the differentiator.
LN: Yeah, certainly if you’re working for a company facing a DOJ inquiry.
CC: Yep.
LN: Knowing the good, the bad, the ugly.
CC: Yep.
LN: As soon as possible can help you make better decisions for your clients. Which might involve, you know, settlement. settling. Yeah, yeah. There have been many recent settlements, recently, from big companies that didn’t want to get tied down at least.
CC: Well I’ve had cases where… One of my favorite ones I used tons of different AI and analytic tools. I had a big bank that had been fined billions of dollars and another big bank was, they had hired on people in that same group, and they were wondering if they would be subject to the same investigation. So, I did some social network analysis. Who was talking to who, with what frequency? I parsed Bloomberg’s chat. I parsed audio logs. And I used everything to keep triangulating down until I was able to identify the bad actors, saying the bad things, and the map of the structured data to show they didn’t do the bad things. And my company wasn’t on the front page of the Wall Street Journal. My company wasn’t fined. So it ends up being very compelling, even early in investigations.
LN: Yeah. Certainly responding quickly is important now. Have you seen any success stories as it relates to companies embroiled with data breach incidences, that have used your platform to help get ahead of what was going on?
CC: 100%. I mean PII, so personally identifiable information, is something that you’re going to have to notify if there is a breach. So if someone, say your Equifax, not that I’m naming them, but say you’re a big company with a lot of personally identifiable or health information. You need to identify it quickly, notify these people in their specific timelines. Tools, like Disco’s, help you use algorithms to find that quickly and act upon it. Otherwise, if you’re looking at 100 million records, there’s no amount of humans that could go through that, in a timely manner, where you’re going to comply with time obligations. And so, it’s majorly impactful.
LN: That certainly is. Well, are there any other things you want to say on the show before we wrap up?
CC: You know, adapt. The reality is no one wants to be the buggy whip maker in a Tesla world. The time to start investigating and vetting and ensuring that the tech you’re looking at isn’t hype is now. Because in a year, or three years, or four years, you might be behind the curve. So, find your resident dork, ask questions, dig into the tech. Now is the time.
LN: And it’s probably worthwhile, you know, without being biased towards Lit Funder, why not take a case try out Disco, try out another offering to see what really works. I mean you had the benefit of…
CC: Yeah.
LN: You were on the other side working for the law firm, shopping for vendors.
CC: I did a 55 vendor RFP. I’ve seen everyone. I’ve looked under every hood. I mean there’s a reason I went to Disco. But there are other tools good out there. I think you want a toolbox with lots of different tools. If you’re a hammer, everything looks like a nail. Let’s be honest, litigation is always bespoke, so you want lots of tools that can help you address it.
LN: Great. Well, thanks again for being on the show.
CC: Yeah, my pleasure.
LN: This was great.
Watch the Entire Series on Artificial Intelligence (AI)
You’re looking for the smoking gun and have tens of thousands of documents to review. Experts Lee Neubecker and ZyLAB’s eDiscovery Director, Jeffrey Wolff say Optimize with AI and make your review easier!
Optimize eDiscovery with AI! Lee Neubecker sets out on a quest to find out what’s happening with Artificial Intelligence as it relates to the eDiscovery review process. Lee visits eDiscovery Director, Jeffrey Wolff from ZyLAB and together they examine how new AI algorithms are coded for priority review and can rank documents for relevance, saving countless hours and dollars for the client. Utilizing new AI will optimize your current eDiscovery process.
Part 2 of 3 Part Series on Smarter Solutions eDiscovery
Part 2 of our 3 Part Series on Smarter Solutions in eDiscovery
Optimize eDiscovery with AI Video Transcript Follows
Lee Neubecker (LN): Hi, I have Jeff Wolff back on the show again from ZyLAB. Jeff, thanks for coming back.
Jeff Wolff (JW): Thank you.
LN: And today we’re going to talk a little bit more about trends in Artificial Intelligence as it relates to eDiscovery and the review process that comes along with that. Jeff, what do you see happening right now with Artificial Intelligence as it relates to the eDiscovery review process?
JW: So what we’ve noticed over time is that, traditionally, Artificial Intelligence was always deemed to be only valid in cases where you had hundreds of thousands or millions of documents. And one of the changes that have happened over the last few years is that the Artificial Intelligence models have gotten so much better than you can now use them for much smaller data sets, and so we evangelize the use of Artificial Intelligence in smaller data sets, even, a thousand documents, you’re going to get a better review, more efficient, and more correct, faster, with AI than you would with a team of reviewers.
LN: So if you have a project and you’re using your platform, let’s say there are a million pages of documents that need to be reviewed. You put a review team on starting that process, and they start categorizing and coding, as they get through the first ten thousand documents, what is your software doing to help make this process more efficient and effective for them?
JW: Sure, so if you’re using traditional, what we call supervised machine learning, that used to be referred to as predictive coding, what our software allows you to do is train a small training batch, so a small sample of the documents, and code them for responsiveness, whether they’re responsive or not responsive. And we’ve made it very easy for users to do that. So, you can create issues, and for each issue, you get two tabs, responsive or not responsive, and you just train, you look through a bunch of training documents and you tag the documents appropriately, and the machine classifier learns, very quickly, what is responsive, what is not responsive. So, maybe after two or at most three training batches, the classifier is now bringing you back almost exclusively responsive documents. It’s already smart enough to do that. And so you only need a few training rounds to get the classifier well over the 80%, typical 80% precision and recall threshold that most attorneys feel is what the human is capable of, but the machine will do 90, 95% precision and recall, so you can be assured, not only are you getting a more efficient and more correct review, but you’re also doing it in a whole lot less time with a whole lot fewer people.
LN: And so, are your algorithms looking for synonyms, and similar phrasing that has equivalent word matches?
JW: It’s a bit of secret sauce. But, yeah, we use a support vector machine-based set of algorithms, kind of the most modern version of machine learning. And it is effective, it understands what our topics that were identified in the document, and what other topics are like them. So that’s how it’s doing an identification. But you’re effectively training in or on that.
LN: So the people using your platform, are they having to necessarily review all of the documents, or are you basically, based on the trained review process, you’re taking that universe of a million, and as they get through it, it’s starting to cluster.
JW: Correct.
LN: There’s a set that, this probably isn’t useful, and you don’t have to look at it, but you can look through it just to see.
JW: Sure.
LN: They have confidence that it’s not excluding relevant stuff, right?
JW: Yeah. What we find from an AI standpoint is that the two primary use cases that attorneys have when they use AI are priority review, so that means hey, I’m going to start teaching the data about, the classifier about my data set, and I’m going to show what responsive documents look like, and then I want it to rank all the remaining documents for me for relevance. And so I’m going to then put eyes on those top-ranking documents. That’s effectively looking for the smoking gun, right? That’s one. But they also use it a lot for QC and this is where I see I’m trying to put a lot more attorneys into utilizing AI, is you’ve already done your tagging, and you had eyes on all of your documents, now go back and use the AI and compare it against what your human reviewers did, and see if you’ve missed things. Because inevitably, your reviewers are not going to be all at the same level. Some people are going to miss-tag documents, and the AI has a really good chance of picking up those mistakes and showing them to you.
LN: So have there been any published studies that document the effectiveness of AI with the review process?
JW: There’s been a bunch of them. I know Law Geeks did one that was pretty interesting. What I’ve read recently is that only about, nationally, about 4% of all cases use Artificial Intelligence officially. But then again, there’s no requirement, in the meet and confer that you identify that you are using Artificial Intelligence in a discovery case. So a lot of attorneys can be used, and just not reporting it. Which is fine, because back when the review was manual, and you went through paper and bankers boxes, you didn’t have to document the process for that review. So why should you have to document the fact that you using a machine to do some of the identification of documents and responsiveness today?
LN: So are there potential problems as a result of using AI for failing to produce relevant documents?
JW: No, I think the case law already demonstrates that AI is an accepted form of using, of identifying reviewed documents, and again, even if you’re just using it for QC purposes, you’re still better off. You’re still less likely to miss things than if you hadn’t used it at all.
LN: Great, well, it’s been great. Thanks a bunch for being on the show.
JW: My pleasure, my pleasure.
View Part 1 of our 3 Part Series on Smarter Solutions in eDiscovery
Artificial Intelligence (AI) can be used to vastly improve the eDiscovery document review process. Zylab is one of several eDiscovery vendors offering solutions utilizing AI. Lee Neubecker, Computer Forensic Expert, and President & CEO of Enigma Forensics met with Jeffrey Wolff, Director of eDiscovery Solutions at ZyLAB during his visit to the Legal Tech Conference 2020 in New York. Lee and Jeffrey discuss how AI can be used to conduct more effective eDiscovery.
Artificial Intelligence (AI) technology is everywhere. It’s hard to imagine how it’s being used in the legal industry where legal libraries filled with law books and courts filled with black-robed judges reign. In this formal traditional world, AI is now providing smart solutions for today’s electronically stored information or ESI and is streamlining the way the Legal Industry works.
In this video, Lee Neubecker, Computer Forensic Expert, and President & CEO of Enigma Forensics met with Jeffrey Wolff, Director of eDiscovery Solutions at ZyLAB during his visit to the Legal Tech Conference in New York. Lee and Jeffrey analyze how Artificial Intelligence (AI) develops smarter solutions in the eDiscovery process. Jeffrey shares with Lee that ZyLAB’s mission is to provide automated full-text retrieval using AI, for both on-premise or cloud-based solutions.
Watch Part 1 of a Three-Part Series on Artificial Intelligence (AI) and eDiscovery.
The video transcript of AI Smarter Solutions: eDiscovery follows.
Lee Neubecker: Hi, I have Jeff Wolff, back on the show from ZyLAB. Jeff, thanks for coming back on.
Jeff Wolff: Thank you.
LN: He’s their Director of eDiscovery, and I wanted to ask him some questions as it related to what differentiates ZyLAB from other products out on the market. Some of my clients may want to use this type of artificial intelligence program to help get through their review and see what the results are of using AI verse the traditional e-discovery review process, so.
JW: Sure.
LN: Jeff, could you tell us what sets ZyLAB apart from other competitors in the marketplace.
JW: Sure, sure, so first, I think ZyLAB is uniquely positioned in the fact we understand the corporate space quite well, as well as the law firm space, but we got our start incorporate, or start in information governance. So we are very vested in search and data science, and that’s really where we’ve put a lot of our focus. We have both on-premise solutions, as well as cloud-based, SaaS solutions like every other next-gen provider. But we really push our interface, our user interface and our user experience, as one of the most unique selling points. And that is, that it is not difficult to start using. Anyone, any legal professional can pick up our product in an hour, from start to finish, and understand really how you utilize it. Drag and drop interfaces for getting data into the system, and immediate color-coding and tagging, easy search, and the ability to really visualize your data and understand what’s in the dataset.
LN: Okay. So, what would you say for a company that has to deal with multiple jurisdictions, they’re in Europe, they’re in the US. JW: Sure. LN: There are some unique challenges posed by all the various regulations out there, like GDPR.
JW: Right.
LN: Maybe the have operations in China. How could you help a company that has to deal with various regulatory authorities spanning the globe?
JW: Sure, and that’s another advantage that ZyLAB has, actually, we’re actually a global company, so we’re dual-headquartered in Washington, D.C., here in the US, as well as Amsterdam in the Netherlands, in the EU. And as a result, we have cloud operations in both jurisdictions. So our global customers can actually keep US data in the US, and they can keep the European Union in the EU, and not worry about that issue. But we also have the expertise, consulting expertise, in both environments, both geographic locations. For example, I’m doing a lot of work now with corporations, not so much focused on directly just on e-discovery, because e-discovery is a bit reactive, you know? Or corporations go through peaks and valleys with e-discovery, the litigation, something they have it, sometimes they don’t. What they constantly have though, are internal investigations, regulatory responses, in the highly regulated corporations. And more and more now, data privacy concerns. So, my European colleagues have been dealing with GDPR for a while, we’re now starting to feel it here in the US, with CCPA, the California Consumer Privacy Act. And there are a number of states on the horizon that are going to California’s examples, so corporations need to be able to find, and classify all the data that they have in their organization that has customer information because if those customers request it and they can’t provide it, they’re financially in a lot of trouble.
LN: Do you think that the regulations coming down on companies are going to fundamentally change how companies chose to communicate with their vendors, suppliers, and own employees?
JW: Absolutely. If you look at all the recent data breach situations, it’s typically not the organization that has the problem, and I won’t mention any of the large companies that have recently had data breaches, but it’s typically not the original company that had the issue, it’s one of their suppliers, or one of their vendors that had accesses to the database, and wasn’t protecting it properly, and that’s how the trouble began.
LN: Yeah.
JW: Same thing with data privacy.
LN: The supply chain certainly is a huge point of vulnerability for all types of organizations. The governments, the military,
JW: Yep.
LN: and even corporations.
JW: Yes.
LN: So what do you see happening over the next few years with the adoption of AI platforms?
JW: I think the e-discovery market is going to fundamentally change. There’s still always going to be a need for discovery within corporations and law firms, but what you do you with the data is going to become much more important, so it’s going to be about how you can extract value from the data, not just metadata, which we’ve always been able to do for years now, but now more about looking for entity information. People, places, organizations that are mentioned in documents and emails, and collaborative environments, and being able to visualize those, and quickly drill down to what was going on in your organization. You know, if you got people that are going to the dentist three times a week, they’re not doing to the dentist, they’re doing something else, They’re just writing about going to the dentist.
LN: Yeah.
JW: Software like ours that can identify those references in documents are going to be crucial to the success of organizations.
LN: That’s great. So it seems that there’s continued e-discovery service provider consolidation out there.
JW: Mmhmm.
LN: The companies that are using tools that are more of a channel partner tool to resell.
JW: Yes.
LN: But as those companies consolidate, do you think that there’s going to be a movement away from those providers where, the company, the firms, directly do their own e-discovery?
JW: Oh, yes. Yeah, very much so. We’ve been seeing that over the last few years. A lot of companies, even small companies that tend to have, in the past, just used outside vendors for e-discovery, are now deciding that they prefer to control, not just the cost, but also their data. They don’t want their data outside of the organization for reasons we’ve already talked about. So they’re purchasing in-house tools that they can use themselves, and then they can invite outside counsel in to make use of, that way they control their costs, they control the efficiency, and they control the data.
LN: Well, this has been great. Thanks a bunch for being on the show.
Forensic Experts Lee Neubecker and Cat Casey from DISCO discuss Artificial Intelligence (AI) as it relates to improving Legal technology.
Artificial Intelligence (AI) thinks, learns and problem solves more efficiently than humans. AI is all around us and in almost everything we touch, it is an algorithm that is designed to make our lives easier and is sometimes referred to as machine learning.
In the case of litigation, it can save time and money by streamlining the process of document review, eDiscovery, and preparation for forensic cases. Computer Forensic Expert, Lee Neubecker and Catherine “Cat” Casey who is the Chief Innovation Officer for DISCO discuss how AI works to improve legal technology.
DISCO is a leader in legal technology is a developer of a cloud-native eDiscovery software for law firms designed to automate and simplify error-prone tasks. They provide a myriad of different types of analytics that will supercharge searching data dramatically reducing time and money.
Part 1 of our Three-Part Series on Artificial Intelligence (AI)
Lee Neubecker (LN): Hi, I’m here today with Cat Casey from CS DISCO. Thanks for being on the show.
Cat Casey (CC): My pleasure.
LN: We’re going to talk a little about artificial intelligence as it relates to eDiscovery and document review. Cat, can you tell us just a little bit about what your firm does to help speed up the review process and lower costs for clients.
CC: Absolutely, we’re a cloud-native AI-powered eDiscovery company. And what that means is we’ve got vast amounts of elastic computational power that we can use to run a myriad of different types of analytics on data to supercharge your searching and dramatically reduce the amount of time it takes you to get to that key actionable evidence. So, we’ve kind of flipped everything on its head. Instead of being a question of how quickly can I read through all of this data, it’s how laparoscopically can I surgically find all of that key information. The results that we’re seeing are pretty resounding. Up to 60% reduction in time to get to that key evidence. Freeing up attorneys to get back to what they went to school for, the practice of law. It’s pretty compelling. We’ve had some pretty interesting additions, including even today, we just announced, I think, the first true AI in eDiscovery with AI model sharing. Basically, with each iteration, with each type of case that you conduct with DISCO, our algorithms are getting smarter. We’re extracting insights and building in more robust taxonomy and analytic structure to parse data, which is going to yield better and better results for our clients. It’s truly exciting.
LN: So we’ve come a long way from the early days when the attorneys wanted everything printed and Bates-labeled before they looked at it. To now, moving ahead using TAR, technology-assisted review, like artificial intelligence, which fits into that, correct?
CC: 100%, we have a continual active learning model, so it’s more reinforcement learning than a standard supervised learning model. Basically, from the coding of document one, our algorithm’s getting smarter and making recommendations on highly likely to be similar documents. We battle test the algorithm on an ongoing basis. Whether it is an affirmative or a negative for a suggested document, the algorithm learns more, and because of that, we prioritize the most relevant information quickly and people are able to then accelerate their review speeds by up to, I think we’ve had over 180 docs per hour. So, it’s pretty compelling and this is just the beginning.
LN: So your platform’s all in the cloud, correct? So companies or law firms, they need no infrastructure other than a browser?
CC: 100%, the nice thing, in my prior life, I ran a global discovery program, and I spent hundreds of thousands of dollars a year just to keep pace, just to have storage, just to have basic replication and back up, and all of that. Now, even a small firm, all the way up to an Am Law One firm or a massive Fortune One company, they can have the same robust technology without having to set up a data center, without having to invest a ton of money. It lets everyone level up and has a better experience throughout the discovery process.
LN: One of the challenges a lot of my clients always have is they have a need to understand what the costs are going to be and to be able to communicate to their clients those expectations so they’re not throwing their clients on the eDiscovery rollercoaster of non-controllable bills. How does DISCO help to address those concerns?
CC: Transparency is a major pain point. One of the banes of my existence used to be trying to normalize this pricing model versus this, versus this service provider, versus this technology. We just throw that all out. We charge one flat amount per gig. It includes analytics. It includes processing. It includes everything, and we work with you to get the volume of data that is being applied to that one flat cost per gig down. It eliminates that hide the ball gotcha moment and it gives a lot of transparency. And of course, if someone wants a different model, we’re happy to accommodate that. But in general, straight, simple, honest. It’s really rewarding for our clients.
LN: So, what cases, what types of litigation case matters do you see as having some of the best benefits of being migrated into your platform?
CC: Yeah, I think any case can. If you’re a tiny company, it helps you be David versus Goliath. Even on a small data volume case, you can start getting insights and reduce the amount of time you’re having to spend doing something maybe you can’t chargeback for. For a big massive case, because we are an AWS and we were built on kind of convolutional neural networking, we’re moving, and we have such a robust computational lift, even we’ve had 150 million documents with hundreds of users and we still have sub one second page to page. We are still lightning fast. And so, whether it’s a big case, a simple case, a complex case, there is a value proposition for almost anyone.
LN: In terms of the types of law firms that are using your platform, do you see many smaller, medium-size firms using your–
CC: Tons, actually tons. That was where we got our teeth. Boutique, we started as a boutique law firm. We actually were a bunch of attorneys that were frustrated that all the tools were terrible, and so they built their own. And so, the foundation of DISCO, we had a family of tons of boutique law firms that we were supporting, we still do to this day. The tool we built though, had a longer vision. It was built to be much bigger and more scalable, and as a result, that’s why you’re seeing us with major, the WilmerHales of the world, very large firms and very large corporations because the tool itself can scale up so much.
LN: Great, what are some of the challenges of working, that law firms find that already have entrenched solutions? There are other review products out there and if they really want to make the benefit of your platform, don’t they have to kind of fully use it for the case?
CC: I would say you probably don’t want to split the baby with a case. If you’re processing with another tool, you’re not going to get the same benefit as working with DISCO. But you don’t have to move your entire litigation portfolio to DISCO day one. We’re seeing a lot of people that are sunsetting Legacy Product and Legacy Platforms moving towards DISCO, but it’s not, “I’m going to move every single case today.” It’s going forward, we’re going to start bringing in new cases. There tends to be such an improved experience and improved UI for the attorneys that they start to not want to use the other technology as much.
LN: I know as a computer forensic expert, oftentimes we’re going out initially collecting and forensically preserving the data. But your product sounds like it would be right for a firm that does forensics that needs to collect different data from computers, possibly harvest just an email. Filter the dates and times of the email to a PST and then they can take those PSTs and upload it into your platform, correct?
CC: 100% and we also, we’ve productized some advanced ECA, where we charge a much, much lower rate. So, you get three months no cost hosting. It’s half the usual rate, and you can do ECA for up to three months. And the goal of that is to let’s whittle down to the most surgical, teeny, tiny, laparoscopic piece of data set that you can have. An example was we had a 20 million document case and we were able to run the ECA, get it down to about 5.6 million documents. Run more coaling, run our analytics, get it down to about 200,000 documents. And usually, that would be when you have to review every single one, but we were able to, with our workflow, with CAL, get it down to 140,000 documents. And so, if you think 50 bucks an hour, an attorney can only do 50 docs an hour, the cost savings is monumental.
LN: So as someone uses your platform and they start to tag and prioritize certain documents, your software learns based on that taking. It helps find related concepts to those conversations and what not?
CC: 100%, 100%.
LN: So really, the more that are reviewed as responsive, similar concepts and whatnot so that important links aren’t missed.
CC: 100% and because we do automatic batching, is every new batch of documents a person gets because we’ve applied this artificial intelligence and continual active learning model, it is a more relevant subset of data and people are able to go through it more faster. And sometimes, they will get to a point where they can say, “I’ve hit all my relevant information. “The rest is not relevant. “I’m going to sample it and statistically determine “I don’t have to review those last 100,000 documents “that maybe aren’t relevant,” and it’s pretty cool.
LN: In our next segment, we’re going to be talking What the trends are in the industry impacting law and eDiscovery. And then finally, we’ll talk about some of the pitfalls of what companies, organizations, and law firms face if they don’t embrace artificial intelligence to help make their review process more efficient. Well, thanks for being on the show.
CC: My pleasure.
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Personal Cell Phone Forensics inlcudes social media, business and personal messages, photos, emails and GPS.
Leading computer forensics Expert Lee Neubecker, discusses the complexities of cell phone forensics with Debbie Reynolds from Debbie Reynolds Consulting. We both agree the litigation involving cell phones becomes personal and proves difficult to gain possession. Personal and business text messages, social media posts, photos, GPS records, emails, are all weaved together and become part of the discovery equation. eDiscovery in today’s era is incomplete without including data from smart phone including text messages, Skype, WhatsApp, Slack, Signal and other messaging platforms. Learn more about eDiscovery as it relates to personal cell phone messaging systems by watching Reynolds and Neubecker discuss the topic in today’s blog video interview.
The video interview transcript follows:
Lee Neubecker: Hi, I’m here today again with Debbie Reynolds, and we’re going to talk about something interesting, which every piece of litigation now is getting into. We’re talking about cell phone forensics. What’s been your experience with litigation involving cell phones and discovery?
Debbie Reynolds: Well, whenever they’re cell phones involved eye-rolling begins because people take their cell phones very personally. As opposed to someone’s laptop, which maybe they don’t want to give up, they will fight tooth and nail not to give up their cell phones. And obviously people, they mix work with pleasure and they’re doing different things. They may not want you to see, even if it’s nothing criminal going on, people just feel very tied to their cell phone. The hardest thing is actually getting possession of it and letting them know that you’re not going to look through their juicy texts or their photographs, especially if it’s not an issue in the case.
Lee Neubecker: I know that whenever you need to get into text messages, it becomes a sensitive topic for people. But there are effective ways to get effective discovery without totally trampling over someone’s privacy in many issues involving contract disputes or other civil litigation, what’s important is to identify the relevant custodians. Let’s say we have your cell phone in the conversation with mine, we can then take that, we can create a single PDF document showing each conversation thread and then you could quickly go through it, if it’s your phone in which your attorney identify relevant, not relevant, and then only take the ones that are between the relevant parties and load that up into the review platform.
Debbie Reynolds: Right. And to one thing, one very effective thing that people are doing now, and that’s something that you do, Lee, is where someone, they don’t want the other side to see their whole cell phone so they’ll have a forensic company collect the phone and say, only give them X. That’s actually a very secure way. It gives people peace of mind knowing that they’re not giving everything over, that the forensic folks can actually do some of this pre-work before people actually start looking at things.
Lee Neubecker: Yeah. And like what I’ve done is, they’re not going to pay me to spend time looking at their photos, nor do I want to look at that stuff.
Debbie Reynolds: No. No one cares. I think that’s what people don’t understand. We’ve been working on cases for over 20 years and I really don’t care what’s on the phone or what you said or what videos on there. It really makes a little difference to us.
Lee Neubecker: What I try to do is I try to quickly create almost a summary index of okay, these are the conversation threads. Tell me which phone numbers are relevant, aren’t relevant, who are the relevant parties, and then we can just pull those specific threads out, put them up into the review platform.
Debbie Reynolds: Exactly.
Lee Neubecker: Now, sometimes there’s issues where photos are relevant specifically, if it’s important that you know the whereabouts or someone on a given date and time. Photos often can establish whether or not someone was really at home sick or out on vacation somewhere. There’s embedded GPS data that is recorded into most photos that are taken with smartphones.
Debbie Reynolds: Unless someone decides to strip it out. I think if you don’t do anything to it, it will collect that data. But there are ways to strip that information out. And also, people can turn off GPS tracking on their phone.
Lee Neubecker: Yeah. Well, thanks for being on the show again today.
