The following list was compiled from interviews with Sheila Antrum (president and senior vice-president, health adult services, UCSF Medical Center), Daphne Stannard (director and chief nurse researcher, Institute for Nursing Excellence, UCSF Medical Center), and Alberto Garcia (patient care director, health adult services,UCSF Medical Center).
Why are we sharing this? UCSF’s Center for Digital Health Innovation was formed three years ago to help improve patient care using technology, and we’d like to see more collaboration between health systems and entrepreneurs in health care. Please reach out to us if you’re working on any of these issues!
1. Patient and family navigation technology. Health systems employ many people as “navigators” to guide patients and their families from one location in a hospital or clinic to another. We like the idea of such technology that in addition to guiding patients through the hospital, helps patients and family members stay abreast of clinical updates. For example, for a patient getting surgery, the app would help guide the patient from the waiting area to the preoperative area and then would later alert the patient’s family members when the surgery has been completed. This solution relies on deep integration with existing hospital technologies, including the electronic health record (EHR) and hospital scheduling software.
2. Virtual hospital sitters. UCSF and other health systems spend millions of dollars each year hiring people to sit at the patient bedside to monitor for falls, self-harm, or other deleterious behaviors. If sitters could be partially replaced with robots or other virtual technology, UCSF could keep patients safe while saving huge amounts of money on workforce costs.
3. Artificial intelligence for the hospital. Advances in machine learning and artificial intelligence (AI) have opened the door for predictive modeling to enhance patient care. From ICU monitoring systems that model patients’ vital signs and produce patient-specific care recommendations, to identifying patterns in care that can have significant impacts on patient outcomes, the future potential for AI in health systems is substantial. Beyond the use of patient data, Sheila and her colleagues expressed interest in AI for the hospital environment – a system in which the environment adapts to patient needs. Such technology could be used for better managing inpatients with pain, delirium, or for promoting mobility for hospitalized patients.
4. Automated documentation within the electronic health record. It’s well accepted that both nurses and physicians spend too much time documenting in electronic health records (EHRs). Nurses, in particular, spend large amounts of time transcribing information from pumps and other patient devices to the EHR. Automated documentation between connected devices and the EHR would improve workforce efficiency and also allow hospitals to more quickly and accurately assess risky states for the institution and the patient, and optimize accordingly. Why hasn’t this happened already? EHR integration between devices is notoriously cumbersome, leaving nurses to take on this effort manually.
5. Virtual home-health communities. Telehealth is rightly a huge area of interest for healthcare innovation. But most telehealth companies are focused on 1:1 patient and provider visits. We see a future in which one provider is able to hold virtual group visits, for example for patients discharged from the hospital with similar conditions. Using population health management tools and the hospital discharge team, these patients could also be matched with a community of patients in their neighborhood, or connect with each other through a virtual platform with relevant resources and communication functions.
6. Price transparency tools for the inpatient setting. There needs to be a better way to track the costs patients face on an ongoing basis. In addition, price transparency tools need to interface with EHRs. Differing reimbursement offered by different plans to each health system have made this issue an ongoing challenge for patients and providers.
7. Pain management dashboard. Current stand-alone technologies exist for chronic disease management, but a pain management dashboard embedded within the EHR could lead to a better and safer tracking of patients with chronic pain issues. With the growing pain medicine epidemic, better and safer pain management is hugely important. Right now, pain management is a fragmented process, requiring patient, pharmacy, nursing, and provider input. An integrated dashboard that allows for cross checking with outpatient pharmacies and the CURES database, pain scoring, and ordering would help ease this currently manual process.
8. Technology to enable safe patient handling. Care teams constantly have to move, position, and lift patients — even when they shouldn’t. And these tasks can be taxing to staff and result in significant hospital liabilities. Similarly, the safe handling of hospital waste is still a very manual process. Robotic technologies that automate these tasks are appealing from a time, cost, and safety perspective.
9. Research management application. For providers and care teams, it is difficult to know if a patient is enrolled in a clinical trial, which can create significant safety issues. For example, if a patient enrolled in a clinical study is admitted to the hospital and is administered a medication that interacts with a study drug, the patient could be at significant risk for a drug interaction. At present, there is no automated way of managing and flagging patients enrolled in trials. With a better research management application that connects to the EHR, an automated research interface would flag patients who are part of a trial and provide additional supporting materials regarding safety measures that must be followed.
10. Apps for frontline staff. Frontline nurses attend to almost all of a patient’s basic needs. In doing so, they often juggle up to 25 pieces of paper with critical information. Ideally, nurses could use charting software that would help manage and streamline all this information.
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The difference between the two terms is not very well understood for one simple reason: There is not a universally agreed upon definition. If you ask 100 different economists to define the terms recession and depression, you would get at least 100 different answers. I will try to summarize both terms and explain the differences between them in a way that almost all economists could agree with.
The standard newspaper definition of a recession is a decline in the Gross Domestic Product (GDP) for two or more consecutive quarters.
This definition is unpopular with most economists for two main reasons. First, this definition does not take into consideration changes in other variables. For example this definition ignores any changes in the unemployment rate or consumer confidence. Second, by using quarterly data this definition makes it difficult to pinpoint when a recession begins or ends. This means that a recession that lasts ten months or less may go undetected.
The Business Cycle Dating Committee at the National Bureau of Economic Research (NBER) provides a better way to find out if there is a recession is taking place. This committee determines the amount of business activity in the economy by looking at things like employment, industrial production, real income and wholesale-retail sales. They define a recession as the time when business activity has reached its peak and starts to fall until the time when business activity bottoms out. When the business activity starts to rise again it is called an expansionary period. By this definition, the average recession lasts about a year.
Before the Great Depression of the 1930s any downturn in economic activity was referred to as a depression. The term recession was developed in this period to differentiate periods like the 1930s from smaller economic declines that occurred in 1910 and 1913. This leads to the simple definition of a depression as a recession that lasts longer and has a larger decline in business activity.
So how can we tell the difference between a recession and a depression? A good rule of thumb for determining the difference between a recession and a depression is to look at the changes in GNP. A depression is any economic downturn where real GDP declines by more than 10 percent. A recession is an economic downturn that is less severe.
By this yardstick, the last depression in the United States was from May 1937 to June 1938, where real GDP declined by 18.2 percent. If we use this method then the Great Depression of the 1930s can be seen as two separate events: an incredibly severe depression lasting from August 1929 to March 1933 where real GDP declined by almost 33 percent, a period of recovery, then another less severe depression of 1937-38. The United States hasn’t had anything even close to a depression in the post-war period. The worst recession in the last 60 years was from November 1973 to March 1975, where real GDP fell by 4.9 percent. Countries such as Finland and Indonesia have suffered depressions in recent memory using this definition.
Now you should be able to determine the difference between a recession and a depression without resorting to the poor humor of the dismal scientists.
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