对于投资者而言,这是一个新高点:利用人工智能解决医疗问题的初创公司。
根据市场调研机构CB Insights的数据,2020年第二季度,医疗人工智能领域的融资金额从上一季度的9.86亿美元猛增至11亿美元,增幅为14%。
其中最大的一笔是Cedar于6 月获得的7700万美元投资。这家初创公司利用机器学习及相关自动化技术,使患者支付医疗费用的过程更轻松。
CB Insights的医疗分析师王禾(音译)告诉《财富》杂志,他认为,最关键的是,机器学习让一些耗时的流程可以自动化执行,例如账单开具和支付流程。
王禾说,随着经济持续下滑,医疗公司在成本方面面临压力,希望利用自动化技术来“削减成本”。这些公司在“人力”上花了很多钱,比如雇佣充当收款人的代理人,或者给保险公司打电话询问幕后财务细节的中间人。尤其是在这个艰难的时期,他们想通过减少员工数量来降低成本。
“每位首席财务官都希望,在未来10至18个月内削减10%的成本结构。”王禾说。
但是,投资一家专门为医疗机构实现日常任务自动化的初创公司,会不会使得初创公司朝美国以外、医疗系统可能不那么复杂的国家扩张呢?
他表示,以他的居住地——上海为例,人们在医院进行常规体检时,只需要使用智能手机和支付宝来支付费用。而在美国,人们可能需要多次打电话给保险公司,支付一大堆账单。而且一般来说,就算你付了钱,这事也不算完,比如可能会出现计算失误问题,而导致新的账单出现。
你可能会争辩说,美国的医疗体系相当糟糕,开支浪费颇多,初创公司只需要帮助国内医疗机构改进IT服务,就可以扩大规模了。无论如何,当大多数医疗IT问题都集中在国内时,初创公司又哪里需要走出美国呢?
但众所周知,风投人士并不仅仅是想投资公司的常规业务,他们投资的目标是,能产生指数级回报的初创公司,而这通常意味着这些公司需要走向全球。(财富中文网)
译者:Shog
对于投资者而言,这是一个新高点:利用人工智能解决医疗问题的初创公司。
根据市场调研机构CB Insights的数据,2020年第二季度,医疗人工智能领域的融资金额从上一季度的9.86亿美元猛增至11亿美元,增幅为14%。
其中最大的一笔是Cedar于6 月获得的7700万美元投资。这家初创公司利用机器学习及相关自动化技术,使患者支付医疗费用的过程更轻松。
CB Insights的医疗分析师王禾(音译)告诉《财富》杂志,他认为,最关键的是,机器学习让一些耗时的流程可以自动化执行,例如账单开具和支付流程。
王禾说,随着经济持续下滑,医疗公司在成本方面面临压力,希望利用自动化技术来“削减成本”。这些公司在“人力”上花了很多钱,比如雇佣充当收款人的代理人,或者给保险公司打电话询问幕后财务细节的中间人。尤其是在这个艰难的时期,他们想通过减少员工数量来降低成本。
“每位首席财务官都希望,在未来10至18个月内削减10%的成本结构。”王禾说。
但是,投资一家专门为医疗机构实现日常任务自动化的初创公司,会不会使得初创公司朝美国以外、医疗系统可能不那么复杂的国家扩张呢?
他表示,以他的居住地——上海为例,人们在医院进行常规体检时,只需要使用智能手机和支付宝来支付费用。而在美国,人们可能需要多次打电话给保险公司,支付一大堆账单。而且一般来说,就算你付了钱,这事也不算完,比如可能会出现计算失误问题,而导致新的账单出现。
你可能会争辩说,美国的医疗体系相当糟糕,开支浪费颇多,初创公司只需要帮助国内医疗机构改进IT服务,就可以扩大规模了。无论如何,当大多数医疗IT问题都集中在国内时,初创公司又哪里需要走出美国呢?
但众所周知,风投人士并不仅仅是想投资公司的常规业务,他们投资的目标是,能产生指数级回报的初创公司,而这通常意味着这些公司需要走向全球。(财富中文网)
译者:Shog
The shiny new thing for investors: startups using artificial intelligence to fix healthcare woes.
Healthcare-related A.I. funding jumped 14% to $1.1 billion in the second quarter of 2020 from $986 million during the previous quarter, according to market intelligence firm CB Insights.
One of the biggest deals in recent months was Cedar’s $77 million round, which it landed in June. That startup uses machine learning and related automation tech to make it easier for patients to pay their healthcare bills.
He Wang, a healthcare analyst for CB Insights, told Fortune that “the biggest theme” he sees is the idea that machine learning is “automating” time-consuming tasks, like those related to billing and payments.
Healthcare firms are especially interested (and under pressure) to use automation tech “to cut costs” as the economy continues to decline, Wang said. Those firms spend a lot of money on “human capital in this space,” or the agents who act as bill collectors or the middlemen who call insurance companies regarding behind-the-scenes financial minutiae. Especially in these trying times, they want to save money by employing less people than they used to.
“Every CFO is looking to cut 10% of their cost structure in the next 10-18 months,” Wang said.
But would an investment in a startup that specializes in automating mundane tasks for healthcare firms “scale” in countries outside the U.S., where healthcare systems may be less complicated?
In Shanghai, where Wang is based, he says people can get a routine physical exam at a hospital and simply pay for the visit using their smartphone and the Alipay service app. Compare that scenario to going to the doctor in the U.S., which can entail multiple phone calls with insurance providers, tons of bills, and a general sense that even after you pay for a visit, something isn’t quite finished—there could be some sort of calculation error resulting in another bill down the road.
You could argue that the healthcare system in the U.S. is so broken, with so much wasted spending, that a startup could become a massive business simply by helping domestic healthcare firms manage their IT better. Who needs the rest of the world when the majority of the healthcare-related IT problems are here in the U.S. anyway?
But we all know that venture capitalists aren’t merely looking to fund your run-of-the-mill software business. They want to put money into a startup that will generate exponential returns—and oftentimes that means these companies need to go global.