Machine Learning and Prediction in Medicine — Beyond the Peak of Inflated Expectations

Big data, we have all heard, promise to transform health care with the widespread capture of electronic health records and high-volume data streams from sources ranging from insurance claims and registries to personal genomics and biosensors. Artificial-intelligence and machine-learning predictive algorithms, which can already automatically drive cars, recognize spoken language, and detect credit card fraud, are the keys to unlocking the data that can precisely inform real-time decisions. But in the “hype cycle” of emerging technologies, machine learning now rides atop the “peak of inflated expectations.”

Prediction is not new to medicine. From risk scores to guide anticoagulation (CHADS) and the use of cholesterol medications (ASCVD) to risk stratification of patients in the intensive care unit (APACHE), data-driven clinical predictions are routine in medical practice. In combination with modern machine learning, clinical data sources enable us to rapidly generate prediction models for thousands of similar clinical questions. From early-warning systems for sepsis to superhuman imaging diagnostics, the potential applicability of these approaches is substantial.

Yet there are problems with real-world data sources. Whereas conventional approaches are largely based on data from cohorts that are carefully constructed to mitigate bias, emerging data sources are typically less structured, since they were designed to serve a different purpose (e.g., clinical care and billing). Issues ranging from patient self-selection to confounding by indication to inconsistent availability of outcome data can result in inadvertent bias, and even racial profiling, in machine predictions. Awareness of such challenges may keep the hype from outpacing the hope for how data analytics can improve medical decision making.

Machine-learning methods are particularly suited to predictions based on existing data, but precise predictions about the distant future are often fundamentally impossible. Prognosis models for HER-negative breast cancer had to be inverted in the face of targeted therapies, and the predicted efficacy of influenza vaccination varies with disease prevalence and community immunization rates. Given that the practice of medicine is constantly evolving in response to new technology, epidemiology, and social phenomena, we will always be chasing a moving target.

The rise and fall of Google Flu remind us that forecasting an annual event on the basis of 1 year of data is effectively using only a single data point and thus runs into fundamental time-series problems. Yet if the future will not necessarily resemble the past, simply accumulating mass data over time has diminishing returns. Research into decision-support algorithms that automatically learn inpatient medical practice patterns from electronic health records reveals that accumulating multiple years of historical data is worse than simply using the most recent year of data. When our goal is learning how medicine should be practiced in the future, the relevance of clinical data decays with an effective “half-life” of about 4 months. To assess the usefulness of prediction models, we must evaluate them not on their ability to recapitulate historical trends, but instead on their accuracy in predicting future events.

Although machine-learning algorithms can improve the accuracy of prediction over the use of conventional regression models by capturing complex, nonlinear relationships in the data, no amount of algorithmic finesse or computing power can squeeze out information that is not present. That’s why clinical data alone have relatively limited predictive power for hospital readmissions that may have more to do with social determinants of health.

The apparent solution is to pile on greater varieties of data, including anything from sociodemographics to personal genomics to mobile-sensor readouts to a patient’s credit history and Web-browsing logs. Incorporating the correct data stream can substantially improve predictions, but even with a deterministic (nonrandom) process, chaos theory explains why even simple nonlinear systems cannot be precisely predicted into the distant future. The so-called butterfly effect refers to the future’s extreme sensitivity to initial conditions. Tiny variations, which seem dismissible as trivial rounding errors in measurements, can accumulate into massively different future events. Identical twins with the same observable demographic characteristics, lifestyle, medical care, and genetics necessarily generate the same predictions — but can still end up with completely different real outcomes.

Though no method can precisely predict the date you will die, for example, that level of precision is generally not necessary for predictions to be useful. By reframing complex phenomena in terms of limited multiple-choice questions (e.g., Will you have a heart attack within 10 years? Are you more or less likely than average to end up back in the hospital within 30 days?), predictive algorithms can operate as diagnostic screening tests to stratify patient populations by risk and inform discrete decision making.

Research continues to improve the accuracy of clinical predictions, but even a perfectly calibrated prediction model may not translate into better clinical care. An accurate prediction of a patient outcome does not tell us what to do if we want to change that outcome — in fact, we cannot even assume that it’s possible to change the predicted outcomes.

Machine-learning approaches are powered by identification of strong, but theory-free, associations in the data. Confounding makes it a substantial leap in causal inference to identify modifiable factors that will actually alter outcomes. It is true, for instance, that palliative care consults and norepinephrine infusions are highly predictive of patient death, but it would be irrational to conclude that stopping either will reduce mortality. Models accurately predict that a patient with heart failure, coronary artery disease, and renal failure is at high risk for postsurgical complications, but they offer no opportunity for reducing that risk (other than forgoing the surgery). Moreover, many such predictions are “highly accurate” mainly for cases whose likely outcome is already obvious to practicing clinicians. The last mile of clinical implementation thus ends up being the far more critical task of predicting events early enough for a relevant intervention to influence care decisions and outcomes.

With machine learning situated at the peak of inflated expectations, we can soften a subsequent crash into a “trough of disillusionment” by fostering a stronger appreciation of the technology’s capabilities and limitations. Before we hold computerized systems (or humans) up against an idealized and unrealizable standard of perfection, let our benchmark be the real-world standards of care whereby doctors grossly misestimate the positive predictive value of screening tests for rare diagnoses, routinely overestimate patient life expectancy by a factor of 3, and deliver care of widely varied intensity in the last 6 months of life.

Although predictive algorithms cannot eliminate medical uncertainty, they already improve allocation of scarce health care resources, helping to avert hospitalization for patients with low-risk pulmonary embolisms (PESI) and fairly prioritizing patients for liver transplantation by means of MELD scores. Early-warning systems that once would have taken years to create can now be rapidly developed and optimized from real-world data, just as deep-learning neural networks routinely yield state-of-the-art image-recognition capabilities previously thought to be impossible.

Whether such artificial-intelligence systems are “smarter” than human practitioners makes for a stimulating debate — but is largely irrelevant. Combining machine-learning software with the best human clinician “hardware” will permit delivery of care that outperforms what either can do alone. Let’s move past the hype cycle and on to the “slope of enlightenment,” where we use every information and data resource to consistently improve our collective health.

Source: The New England Journal of Medicine

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As Robots Take Over We Will Need More Innovators

AnalyticsAnywhere

The Hadrian X robot is made by Fastbrick Robotics from Australia. It can lay 1000 house bricks in an hour (video below). The average bricklayer lays around 500 bricks a day. We will soon see robots doing much of the standard work in building assembly with a small number of skilled craftsmen supervising them, applying finishing touches or completing tricky tasks. McDonald’s is trialing a “Create Your Taste” kiosk – an automatic system that lets customers order and collect their own configuration of burger meal with no assistant needed.

But it is not just manual labour which will be affected by the inexorable roll out of robots, automation and artificial intelligence. The impact will be felt widely across skilled middle class jobs including lawyers, accountants, analysts and technicians. In many financial trading centres traders have already been replaced by algorithms. The world’s first ‘robot lawyer’ is now available in 50 states.

The World Economic Forum predicts that robotic automation will result in the net loss of more than 5m jobs across 15 developed nations by 2020. Many think the numbers will be much higher. A report by the consultancy firm PWC found that 30% of jobs were potentially under threat from breakthroughs in artificial intelligence. In some sectors half the jobs could go.

The rise of the robots will lead to an increase in the demand for those with the skills to program, maintain and supervise the machines. Most companies will have a Chief Robotics Officer and a department dedicated to automation. However, the human jobs created will be small fraction of the jobs which the robots will replace.

Any job that involves the use of knowledge, analysis and systematic decision making is at risk. Robots can not only absorb a large body of knowledge and rules. They can also adapt and learn on the job.

Where does that leave the displaced humans? The standard answer is education. Policy makers advise that people should retrain into higher skilled professions. The problem is most training simply provides more knowledge and skills which can also be replaced by automation.

“So what jobs can robots not do? Einstein said, ‘Imagination is more important than knowledge.’ It is in the application of imagination that humans have the clear advantage.”

Here are some things which robots do not do well:
1.Ask searching questions.
2.Challenge assumptions about how things are done.
3.Conceive new business models and approaches.
4.Understand and appeal to people’s feelings and emotions
5.Design humorous, provocative or eye-catching marketing campaigns.
6.Deliberately break the rules.
7.Inspire and motivate people.
8.Set a novel strategy or direction.
9.Do anything spontaneous, entertaining or unexpected.
10.Anticipate future trends and needs.
11.Approach problems from entirely new directions
12.Imagine a better future.

Let’s leave the routine knowledge jobs to the robots and focus on developing our creative skills. The most successful organisations will be those that combine automation efficiency with ingenious and appealing new initiatives. We will need more imaginative theorists, more lateral thinkers, more people who can question and challenge. We will need more innovators.

Source:innovationexcellence.com

The meaning of life in a world without work

As technology renders jobs obsolete, what will keep us busy? Sapiens author Yuval Noah Harari examines ‘the useless class’ and a new quest for purpose.

AnalyticsAnywhere

Most jobs that exist today might disappear within decades. As artificial intelligence outperforms humans in more and more tasks, it will replace humans in more and more jobs. Many new professions are likely to appear: virtual-world designers, for example. But such professions will probably require more creativity and flexibility, and it is unclear whether 40-year-old unemployed taxi drivers or insurance agents will be able to reinvent themselves as virtual-world designers (try to imagine a virtual world created by an insurance agent!). And even if the ex-insurance agent somehow makes the transition into a virtual-world designer, the pace of progress is such that within another decade he might have to reinvent himself yet again.

The crucial problem isn’t creating new jobs. The crucial problem is creating new jobs that humans perform better than algorithms. Consequently, by 2050 a new class of people might emerge – the useless class. People who are not just unemployed, but unemployable.

The same technology that renders humans useless might also make it feasible to feed and support the unemployable masses through some scheme of universal basic income. The real problem will then be to keep the masses occupied and content. People must engage in purposeful activities, or they go crazy. So what will the useless class do all day?

One answer might be computer games. Economically redundant people might spend increasing amounts of time within 3D virtual reality worlds, which would provide them with far more excitement and emotional engagement than the “real world” outside. This, in fact, is a very old solution. For thousands of years, billions of people have found meaning in playing virtual reality games. In the past, we have called these virtual reality games “religions”.

What is a religion if not a big virtual reality game played by millions of people together? Religions such as Islam and Christianity invent imaginary laws, such as “don’t eat pork”, “repeat the same prayers a set number of times each day”, “don’t have sex with somebody from your own gender” and so forth. These laws exist only in the human imagination. No natural law requires the repetition of magical formulas, and no natural law forbids homosexuality or eating pork. Muslims and Christians go through life trying to gain points in their favorite virtual reality game. If you pray every day, you get points. If you forget to pray, you lose points. If by the end of your life you gain enough points, then after you die you go to the next level of the game (aka heaven).

As religions show us, the virtual reality need not be encased inside an isolated box. Rather, it can be superimposed on the physical reality. In the past this was done with the human imagination and with sacred books, and in the 21st century it can be done with smartphones.

Some time ago I went with my six-year-old nephew Matan to hunt for Pokémon. As we walked down the street, Matan kept looking at his smartphone, which enabled him to spot Pokémon all around us. I didn’t see any Pokémon at all, because I didn’t carry a smartphone. Then we saw two others kids on the street who were hunting the same Pokémon, and we almost got into a fight with them. It struck me how similar the situation was to the conflict between Jews and Muslims about the holy city of Jerusalem. When you look at the objective reality of Jerusalem, all you see are stones and buildings. There is no holiness anywhere. But when you look through the medium of smartbooks (such as the Bible and the Qur’an), you see holy places and angels everywhere.

The idea of finding meaning in life by playing virtual reality games is of course common not just to religions, but also to secular ideologies and lifestyles. Consumerism too is a virtual reality game. You gain points by acquiring new cars, buying expensive brands and taking vacations abroad, and if you have more points than everybody else, you tell yourself you won the game.

You might object that people really enjoy their cars and vacations. That’s certainly true. But the religious really enjoy praying and performing ceremonies, and my nephew really enjoys hunting Pokémon. In the end, the real action always takes place inside the human brain. Does it matter whether the neurons are stimulated by observing pixels on a computer screen, by looking outside the windows of a Caribbean resort, or by seeing heaven in our mind’s eyes? In all cases, the meaning we ascribe to what we see is generated by our own minds. It is not really “out there”. To the best of our scientific knowledge, human life has no meaning. The meaning of life is always a fictional story created by us humans.

In his groundbreaking essay, Deep Play: Notes on the Balinese Cockfight (1973), the anthropologist Clifford Geertz describes how on the island of Bali, people spent much time and money betting on cockfights. The betting and the fights involved elaborate rituals, and the outcomes had substantial impact on the social, economic and political standing of both players and spectators.

The cockfights were so important to the Balinese that when the Indonesian government declared the practice illegal, people ignored the law and risked arrest and hefty fines. For the Balinese, cockfights were “deep play” – a made-up game that is invested with so much meaning that it becomes reality. A Balinese anthropologist could arguably have written similar essays on football in Argentina or Judaism in Israel.

Indeed, one particularly interesting section of Israeli society provides a unique laboratory for how to live a contented life in a post-work world. In Israel, a significant percentage of ultra-orthodox Jewish men never work. They spend their entire lives studying holy scriptures and performing religion rituals. They and their families don’t starve to death partly because the wives often work, and partly because the government provides them with generous subsidies. Though they usually live in poverty, government support means that they never lack for the basic necessities of life.

That’s universal basic income in action. Though they are poor and never work, in survey after survey these ultra-orthodox Jewish men report higher levels of life-satisfaction than any other section of Israeli society. In global surveys of life satisfaction, Israel is almost always at the very top, thanks in part to the contribution of these unemployed deep players.

You don’t need to go all the way to Israel to see the world of post-work. If you have at home a teenage son who likes computer games, you can conduct your own experiment. Provide him with a minimum subsidy of Coke and pizza, and then remove all demands for work and all parental supervision. The likely outcome is that he will remain in his room for days, glued to the screen. He won’t do any homework or housework, will skip school, skip meals and even skip showers and sleep. Yet he is unlikely to suffer from boredom or a sense of purposelessness. At least not in the short term.

Hence virtual realities are likely to be key to providing meaning to the useless class of the post-work world. Maybe these virtual realities will be generated inside computers. Maybe they will be generated outside computers, in the shape of new religions and ideologies. Maybe it will be a combination of the two. The possibilities are endless, and nobody knows for sure what kind of deep plays will engage us in 2050.

In any case, the end of work will not necessarily mean the end of meaning, because meaning is generated by imagining rather than by working. Work is essential for meaning only according to some ideologies and lifestyles. Eighteenth-century English country squires, present-day ultra-orthodox Jews, and children in all cultures and eras have found a lot of interest and meaning in life even without working. People in 2050 will probably be able to play deeper games and to construct more complex virtual worlds than in any previous time in history.

But what about truth? What about reality? Do we really want to live in a world in which billions of people are immersed in fantasies, pursuing make-believe goals and obeying imaginary laws? Well, like it or not, that’s the world we have been living in for thousands of years already.

Source: The Guardian

Self-driving AI clinic reimagines healthcare for the 21st century

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Seattle-based design firm Artefact Group has revealed a comprehensive concept that would make the future of healthcare mobile. Integrating passive monitoring technologies in the home, a smartphone app, AI diagnostics and a self-driving clinic, the system combines a variety of innovations for a new spin on healthcare.

While many sectors of society are being dramatically disrupted by rapidly evolving digital innovations, the arena of healthcare seems to responding more slowly, with many hospitals still largely relying on paper to record patient data. Earlier in the year we saw a gadget-filed, subscription-based medical clinic open in San Francisco, and several fascinating advances are occurring in the field of artificial intelligence diagnostics, But the Aim concept envisions a fundamentally different healthcare approach than what we have been used to for the past 100 years.

The system begins with a series of active testing and passive monitoring devices in the home, capturing data from several sources, such as the bathroom scale, toilet and medicine cabinet. The goal is to create an interconnected set of devices, including health-monitoring wearables, that can create a unified, patient-owned health record.

A constantly learning AI would then monitor a person’s health data and flag unusual results. When needed, a self-driving mini clinic could navigate to your location for more comprehensive diagnostics, such as thermography, breath analysis, and respiration or cardiac rhythm.

Inside this mobile clinic, an AI could offer its diagnosis, and even deliver common pharmaceuticals such as antibiotics or contraceptives. If a health condition is flagged as serious or escalating, the Aim system would then connect the patient to an on-call specialist or even transport them directly to a hospital emergency room.

“The mission of Aim is to close the data, experience and logistical gaps between home and clinical environments,” the designers say.

Despite being a slightly pie-in-the-sky concept right now, rapid advances in personal health monitoring and AI means it’s not necessarily that far from being feasible, and much of the Aim system feels like it could be pragmatically implemented into our current healthcare processes without too much trouble. With the current burden on patients to get to doctors’ clinics, which can sometimes be quite far away, an integrated monitoring system such as this could lighten the load for overworked healthcare workers.

AI-driven diagnostic tools are also set to inevitably become increasingly useful for low-risk patient monitoring, and a mobile autonomous clinic could significantly reduce the drain on current hospital resources by catching conditions early before they become serious enough to require a hospital admission.

Cost is of course a major consideration here and developing such a sophisticated system wouldn’t be cheap, but as the costs of healthcare continue to skyrocket maybe some outside-the-box thinking such as this is should be encouraged. Much like the San Francisco Forward clinic, a cost-effective subscription-based system could possibly offer many who currently can’t afford big health insurance premiums greater access to medical care.

Source: newatlas.com

5 Facebook Bots To Support Your Health

AnalyticsAnywhere

HealthTap — A Doctor Bot

HealthTap is a larger health company that decided to democratize their wealth of health information by creating a chatbot.

You can ask all your burning medical questions here and get resources from HealthTap’s large database, as well as personalized responses from doctors! No more waiting rooms, the chatbot will see you now.

Atlas — A Fitness Bot

Atlas knows how hard it can be to keep up with a regular workout routine when your days are getting swamped.
Maybe not for pro-athletes but definitely for fitness enthusiasts of all kinds, Atlas is free and has more than a few tricks up his sleeve to keep you engaged with your workouts. This bot is currently in beta and sends personalized workout reminders on a schedule you provide along with motivational quotes (#justdoit). Very promising concept inside a bot and the Atlas makers plan to expand into workout plans and fitness tips very soon! Stay tuned.

Woebot — A Mood Bot

Woebot is a mood tracking bot with personality and a conversation designed that feels like talking to a bot therapist.
Backed by scientific research, Woebot can help reduce depression, share CBT resources, and learns from your conversations over time. The Woebot makers offer scalable pricing for individuals and the first 14 sessions are totally free.

Forksy — A Nutrition Bot

Forksy keeps track of your meals so you don’t have to. Whether you had three slices of pizza or a bagel with a little too much cream cheese, Forksy knows the dirty secrets of your diet. If you’re trying to be more health conscious, Forksy is a great option. The NLP capabilities are great and it feels as if you can just type in any food combination and get an instant result.

Izzy — A Period Tracker Bot

Izzy helps women track their periods and sends reminders to take birth control pills. This chatbot has a fun personality and tries to turn a not-so-fun topic into something more friendly and manageable.
It would be great to see clever NLP for topics unrelated to menstruation but Izzy takes on a great use case to bring chatbots closer to women.

Why Chatbots & Health? — Key Takeaway

Health apps and wearable devices took the world by storm, supporting users throughout their daily activities. It seemed normal in the era of the app store to download and try a couple of new apps on a weekly basis but app downloads have been steadily decreasing over the past years.

Chatbots are booming and bot developers are finding more use cases in different health industries. Some of the bots mentioned above are not as in-depth and far-reaching as their app competitors but chatbots in general seem to be a great solution for simple activities and quick feedback.

Why? There’s no need to require a Facebook user to leave Facebook and open another application to support a simple task. Chatbots don’t require that.

Brief explainer: I’m referring to Facebook’s current challenge to retain its users on the Facebook application and not lose them to other apps such as a fitness application when a user wants to get workout suggestions.

Implementing chatbots on Facebook creates a retention ecosystem, something that Facebook’s Chinese competitor WeChat has mastered. Users don’t have to leave Facebook anymore.

With such seamless yet effective interactions, chatbots are here to make our lives easier in different ways. The list above shows us that chatbots can help us stay on top of our fitness routine, track periods, track moods, provide us with dietary feedback, connect us with doctors and a lot more.

It seems inevitable that we will see a wave of chatbots disrupting the health space (& other industries) with users finding more ways to support their daily activities within the platforms they spend most of their time on such as Facebook.

Source: chatbotsmagazine.com

The Human Army Using Phones to Teach AI to Drive

AnalyticsAnywhere

As her fellow patients read dog-eared magazines or swipe through Instagram, Shari Forrest opens an app on her phone and gets busy training artificial intelligence.

Forrest isn’t an engineer or programmer. She writes textbooks for a living. But when the 54-year-old from suburban St. Louis needs a break or has a free moment, she logs on to Mighty AI, and whiles away her time identifying pedestrians and trash cans and other things you don’t want driverless cars running into. “If I am sitting waiting for a doctor’s appointment and I can make a few pennies, that’s not a bad deal,” she says.

The work is a pleasant distraction for Forrest, but absolutely essential to the coming ages of driverless cars. The volume of data needed to train the AI underpinning those vehicles staggers the imagination. The Googles and GMs of the world rarely mention it, but their shiny machines and humming data centers rely on a growing, and global, army of people like Forrest to help provide it.

You’ve probably heard by now that almost everyone expects AI to revolutionize almost everything. Automakers in particular love this idea, because robocars promise to increase safety, reduce congestion, and generally make life easier. “The automotive space is one of the hottest and most advanced fields applying machine learning,” says Matt Bencke, CEO of Mighty AI. He won’t name names, but claims his company is working with at least 10 automakers.

The challenge lies in teaching a computer how to drive. The DMV rule book provides a good place to start, because it covers rudimentary things like “Yield to pedestrians.” Ah, but what does a pedestrian look like? Well, a pedestrian usually has two legs. But a skirt can make two legs look like one. What about a fellow in a wheelchair, or a mother pushing a stroller? Is that a small child, or a large dog? Or a trash can? Any artificial intelligence controlling a two-ton chunk of steel must learn how to identify such things, and make sense of an often confusing world. This is second nature for humans, but utterly foreign to a computer.

Cue Forrest and 200,000 other Mighty AI users around the world.

The onboard cameras helping prototype robocars navigate the world photograph almost every environment and circumstance you can image. Automakers and tech companies send those photos by the millions to an outfit like Mighty AI, which makes a game of identifying everything in those photos. It sounds tedious, but Mighty AI makes it a 10 minute task with points, skills, and level-ups to keep it engaging. “It’s more like Candy Crush than a labor farm,” says Bencke. The monetary rewards, although small, help, too.

Forrest carefully draws a box around every person in each picture, then around every approaching car, and then around the tires on each car. That done, she zooms in, and working pixel-by-pixel, meticulously outlines things like trees. Click click, click. She selects a different color pointer and highlights traffic lights, a telegraph pole, a safety cone. When she’s finished, the scene is annotated in language a computer understands. Engineers call it a “semantic segmentation mask”.

The need for accuracy makes for painstaking work, but Forrest, who makes a few centers per picture, enjoys it. “It’s like why some adults color,” she says. “It’s become a relaxing task.”

Those millions of annotate photos help an AI identify patterns that help it understand, say, what a human looks like. Eventually the AI grows smart enough to draw boxes around pedestrians. People like Forrest will help double-check the AI’s work. Over time, AI will grow smart enough to reliably identify, say, kangaroos.

Relying on an army of amateurs might seem odd, but it remains the most efficient way of training AI. “It’s pretty much the only way,” says Premkumar Natarajan, who specializes in computer vision at the USC Information Sciences Institute. He’s been working in the field for more than two decades. Although there’s been some promising research into so-called unsupervised learning where computers learn with minimal input, but for now the intelligence in artificial intelligence depends on the quality of the data its trained on.

Bencke says his platform uses its own machine learning to determine what each member of the Mighty AI community is best at, then assign them those jobs. No one is getting rich doing this essential work, but for Forrest, that’s beside the point.

She says she made about $300 last year, money she put toward online shopping. She’s never seen an autonomous vehicle, much less ridden in one. But knowing that she’s helping make them smarter will make her more likely to trust the technology when she finally does.

Source: Wired

How governments are adopting modern business intelligence

Data analysis company Tableau calls on state and local government leaders to embrace modern business intelligence platforms to help scale services more efficiently.

Efficiency and scalable impact might not be the first things that come to mind when you think of government. A basic, and inherent connotation is to think of government as slow and bureaucratic. But that’s the old paradigm — the path without data.

In 1995, when former President Bill Clinton signed the Government Performance and Results Act (GPRA) into law, federal agencies began shifting their strategy to measure and report on program performance. Data, not gut feel, became the new foundation for improving citizen services, increasing accountability, and driving mission goals.

Twenty years later, the shift to data is further amplified in part due to additional legislation, like the Digital Accountability and Transparency Act (DATA), signed into law by former President Barack Obama in 2014. This mandated transparency — where all Federal agencies are required to share data sources, critical insights and reports on matters like budget spend with the public — is gaining traction. However, factors beyond legislation are pushing governments to become more data-centric.

The newest catalyst driving governments to a data-driven approach is the emergence of modern business intelligence (BI), a methodology that empowers everyone within an organization to access and analyze the data they need. This modern, self-service approach to analytics enables an easier and faster way for both employees and leaders to measure performance metrics across every program in the agency.

Mark Russell, a contracted analyst and systems administrator at the Florida’s Department of Juvenile Justice (FDJJ), is one of those government leaders changing the way people think about data-led government efficacy. By turning to self-service analytics, Russell is working to empower the government workers in his agency with a 360-degree view of juvenile offenders that are at risk of falling deeper into the system. By giving everyone access to data and insights, workers are better equipped to take action faster.

“When [our data-driven program] works, people develop an expectation that we can get stuff done. That reputation contributes to a view of good government. It means lawmakers trust our input when developing bold juvenile justice reform policies, and have faith in us to carry out those policies. We’re delivering outcomes for the ‘business’ of government,” Russell said.

In the past, when governments relied on traditional BI, IT managed the reporting queue and struggled to keep up with business questions — making timely and trustworthy insights almost impossible.

For the FDJJ, compiling a detailed report about every juvenile offender and their current standing within the system used to take up to four to five months to create. When Russell’s team implemented a modern business intelligence platform, the speed to insight was reduced to two days.

The FDJJ also used self-service data visualization to create the Prolific Juvenile Offenders dashboard, which delivers a complete view of Florida’s most at-risk juveniles. This dashboard enables everyone in the FDJJ, from caseworkers to agency leaders, to drill down into the specifics and understand where an individual is physically located, what offenses they committed, and what treatments they are receiving. The dashboard was further enhanced by a data-sharing agreement with the Department of Children and Families (DCF). Adding this additional layer of transparency allows an even better view into at-risk youth and helps coordinate intervention strategies between the FDJJ and DCF.

Additionally, the FDJJ uses insights from this dashboard to directly influence policy by showing legislators and other stakeholders the impact policy changes have on the budget, and ultimately the at-risk youth. For example, when juveniles are in the community, the visibility from the dashboard triggers more contact from caseworkers and parole officers. And because the dashboard is updated every six hours, workers in the field — like parole officers and even direct supervisors — have real-time information to effectively and quickly manage caseloads.

The Florida Department of Juvenile Justice isn’t alone in its success with modern BI. Governments around the world are using modern analytics platforms like Tableau to deliver more with less. With modern BI, anyone in a government agency can use data to see and understand exactly how programs are performing. And the results are amazing.

Source: statescoop.com