Factors Affectng Outsourcng for Informaton Technology Servces n Rural Hosptals: Theory and Evdence Bran E. Whtacre Department of Agrcultural Economcs Oklahoma State Unversty bran.whtacre@okstate.edu J. Matthew Fannn Department of Agrcultural Economcs Lousana State Unversty mfannn@agcenter.lsu.edu James N. Barnes Department of Agrcultural Economcs Lousana State Unversty jbarnes@agcenter.lsu.edu Workng paper prepared for Southern Agrcultural Economcs Assocaton Annual Meetng n Dallas, Texas Feb 2 5, 2008. Copyrght 2008 by Bran E. Whtacre, J. Matthew Fannn, and James N. Barnes. All rghts reserved. Readers may make verbatm copes of ths document for non-commercal purposes by any means, provded that ths copyrght notce and ths statement appear on all such copes. Abstract As health nformaton technology becomes more prevalent for most healthcare facltes, hosptals across the naton are choosng between performng ths servce n-house and outsourcng to a technology frm n the health ndustry. Ths paper examnes factors affectng the nformaton technology (IT) outsource decson for varous hosptals. Usng 2004 data from the Amercan Hosptal Assocaton, logstc regresson models fnd that governmental ownershp and a proxy varable for hosptals that treat more severe njures postvely mpact the probablty of outsourcng for IT servces. Key Words: Health Informaton Technology, Outsourcng, Hosptal JEL Classfcatons: I12, C140 1
1. Introducton Organzaton of servces at rural hosptals s an mportant aspect of hosptal performance and largely affects the qualty of patent care. For example, the cost of admnsterng medcal bllng, patent records, and health care safety and regulatory complance contnues to be a sgnfcant burden to rural hosptals n the U.S. Whle numerous studes have documented the potental benefts from the use of nformaton technology (IT) n a hosptal envronment (Brooks et al, 2005; Bates et al, 2001; Haux, Wnter, Ammenwerth, Brggel, 2004), very lttle research has looked nto how hosptals should go about mplementng ths technology. Changes n rural health based programs usually ncrease relevant admnstratve costs, compellng rural hosptal admnstrators to evaluate several busness strateges for managng dgtal nformaton. These strateges consst of ether hrng IT staff as salared employees or outsourcng for such servces to a technology frm n the health ndustry. Because future legslaton may set the stage for data management n all hosptals, understandng the characterstcs that lead to outsourcng should help hosptal admnstrators determne whch factors are most nfluental to ther own decson process. We examne the outsource decson n the rural hosptal settng and evaluate the factors that affect the use of outsourcng for IT by rural hosptal admnstrators nstead of employng an nternal IT staff. The examnaton of rural health servces from an economc organzaton perspectve has receved lttle attenton hstorcally. However, a few studes have begun to apply transacton cost theory to understand the procurement of 2
servces, ncludng recrutment of physcans to rural communtes (Fannn and Barnes, 2007). To our knowledge, ths paper represents one of the frst emprcal applcatons of the conceptual model of transacton cost theory to understand the organzaton of IT servces n rural hosptals n the U.S. Ths approach represents a new way of thnkng about the IT adopton decson n that t documents and emprcally estmates the mportance of transacton costs for the varous IT optons avalable. Objectve and Background Ths paper focuses on the factors that affect whether or not rural hosptals outsource ther IT work. Specfc objectves nclude: (1) to develop a conceptual model of transacton cost theory that explans the outsource decson by rural hosptal admnstrators when procurng nformaton technology servces; (2) to dentfy transacton cost theory hypotheses to be tested; (3) to dentfy the key drvers of outsourcng for IT servces n rural hosptals n the U.S.; and (4) to dscuss the polcy mplcatons assocated wth enhancng the adopton of IT assets wthn rural hosptals. Addtonally, as telemedcne servces such as teleradology and telepsychatry become more common for many rural hosptals, ths paper looks nto some of the ssues assocated wth combnng these applcatons nto the exstng IT structure and whether that work should be outsourced or done nternally. As healthcare nformaton systems have become more and more complex, a growng body of research has evolved on ths topc. The benefts of movng to such systems have been well-documented, and nclude error reducton, cost mnmzaton, and more effcent 3
tme management (Bates et al. 2001, Brooks et al 2005, Hauz et Al, 2004). Further studes on the dffuson and adopton of health IT suggests that despte common belefs that such technology wll ncrease healthcare savngs, reduce medcal errors, and mprove overall health status, lttle progress has been made n the actual adopton of IT servces such as Electronc Medcal Records (EMRs) and Clncal Decson Support Tools. In fact, by 2005 only 20 to 30 percent of hosptals have commtted to adoptng IT servces (Fonkych and Tayler, 2005). Some studes have focused on why those hosptals that dd adopt health IT servces chose to do so. These studes have generally uncovered a postve relatonshp between the fnancal status, sze, and productvty of the health care faclty and ts level of IT adopton. Not surprsngly, hosptals that are more wred are often more productve and have better control of ther fnancal stuaton (Solovy, 2001). However, endogenety problems made nferences about causalty dffcult n fact Parente and Dunbar (2001) concluded that health IT had no mpact on a hosptal s operatng margn. Other studes have found several factors that seem to have an mpact on IT adopton tself, such as for-proft status (negatvely assocated), operaton n a compettve envronment (postvely assocated), hgher caseloads of sck patents (postvely assocated), and tme under operaton (mxed results) (Parente and Van Horn, 2003; Wang et al, 2002). Further, Borzekowsk (2002) found that the mplementaton of Medcare s prospectve payment system ncreased the rate of IT adopton for hosptals. Addtonal studes have questoned whch of these servces are most lkely to be outsourced, and why. Wholey et al (2001) found that the development of an IT system s much more lkely to be outsourced than the day-to-day operaton of the system. The dea 4
s a hosptal admnstrator would choose to buy the IT system from an IT company rather than nvestng ts own resources to develop a propretary IT system. Such a decson to outsource for IT depends on several factors. From a hosptal perspectve, how can we understand the possbltes? In what follows, we use transacton cost theory as our conceptual framework to understand the economc ncentve factors that drve the decson to outsource for IT servces n hosptals. Conceptual Framework Transacton cost theory (TCT) (Coase, 1937; Wllamson, 1991) assumes people have lmted knowledge of future events and act opportunstcally. The transacton cost to avod n contractng wth another frm are those related to what Wllamson calls opportunsm. Wllamson developed a reduced form model that hghlghts the relatve cost of outsourcng versus n-house producton of servces. For example, the reduced form model uses three economc ncentve factors as determnants as to why a hosptal would choose to outsource for IT servces compared to provdng those same servces va nternal procurement wth a set of employees who would be responsble for mantanng IT servces. Hence, the hosptal faces a make (provde nternally va employees) or buy (from an IT frm) decson for IT servces. TCT uses uncertanty, frequency and asset specfcty as three prmary determnants that explan the make or buy decson for IT. Frequency refers to how often a transacton wll take place to provde servces. For a hosptal, t mght represent the number of tmes IT servces would be purchased from an IT based frm for hosptal operatons. Accordng to 5
TCT, the hgher the frequency, the more lkely a hosptal would consder hrng employees to provde IT servces n-house. Hence, frequency would usually have a negatve effect on outsourcng for IT servces assumng the cost of outsourcng exceeds that of n-house producton. Uncertanty can refer to demand, technologcal or envronmental dmensons that affect outsourcng. For example, a hosptal admnstrator would consder the performance record of an IT frm before decdng to outsource for IT servces to avod poor qualty servce and cost overruns. Hgh performance uncertanty rases the cost of dong busness and a hosptal may be better off hrng ts own employees and provdng IT servces n-house. Hence, hgh performance uncertanty tends to ncrease the lkelhood that a hosptal would choose to provde servces n-house because the cost of outsourcng exceeds n-house producton. Fnally, TCT uses alternatve types of what Wllamson calls asset specfcty to understand the relatve cost of usng outsourcng compared to n-house producton. The two prmary types of asset specfcty varables used n TCT studes relate to nvestments made n human and physcal assets. Hgh asset specfcty means the hosptal makes a szeable nvestment n an IT system specfcally desgned for ts busness operatons; hence, swtchng costs for the hosptal to swtch to another IT system would be hgh. The IT frm, knowng such an IT system has been developed for the unque hosptal operatons, could hold-up the hosptal for more value at contract renegotaton. Gven swtchng costs are hgh, the IT frm would have some opportunty to capture more of a margn. Knowng ths type of opportunsm by the IT frm exsts, the hosptal could opt to go wth a less specalzed IT system to avod the costs of hold-up. Hence, TCT suggests f 6
the hosptal nvests n a hghly-specfc IT system, more control over that system would be preferred to less and ths usually means hrng employees and developng an IT system n-house. Provdng n-house IT servces would then avod the added cost of hold-up assocated wth outsourcng to the IT servces frm. One type of drect cost of hold-up could be a hgher upgrade prce (than otherwse was agreed to) for the software that organzes the IT nfrastructure. An ndrect cost of hold-up could be the opportunty costs assocated wth not havng the updated IT system, ncludng effects to patent qualty of care, hrng and managng day-to-day operatons. In ths paper, we follow the recent work of Esposto (2004) to dentfy possble TCT factors that determne the outsource decson by hosptals for IT servces for two reasons. Frst, TCT has become the predomnantly used ndustral organzaton theory to understand outsource decsons by frms and Esposto represents a useful applcaton of how TCT explans outsource decsons n hosptals. Second, whle Esposto studed the outsource decson related to procurng physcan servces, we beleve the TCT factors consdered correspond to other outsource decsons such as IT. The key TCT factor consdered by Esposto was not asset specfcty or frequency, but that of complexty. Esposto examned the outsource decson for physcan servces as determned by the complex nature of the average procedure provded by physcans. The use of a case mx ndex was used by Esposto for complexty of servces rendered by physcans; the case mx ndex s used by the Centers for Medcare and Medcad Servces n the Medcare Prospectve Payment System to compensate hosptals for the 7
treatment servces provded by physcans. Specfcally, each tme a physcan treats a Medcare patent, hs or her medcal record has to be updated for dagnoses and procedures; approxmately 13,000 dagnoses and 5,000 procedures exst all of whch are represented by specfc codes. Based on treatment, each patent s classfed nto a group of these codes called Dagnoses Related Group (DRG). The hosptal then receves a payment whch s called the DRG payment and s calculated by multplyng the weght (assocated wth a DRG whch hs set by the Centers for Medcare and Medcad Servces) by the payment rate whch s set by federal regulatons for dfferent types of hosptals. The case mx ndex (CMI) represents the average DRG weght for all of a hosptal s Medcare volume, can be nterpreted as a relatve severty measure of a patent populaton and s drectly proportonal to DRG payments. Esposto cted the CMI as understandng the complex nature of managng patents wth dfferent scknesses and medcal needs. Esposto found a postve relatonshp between the CMI and the use of physcan contractual arrangements whch provded the hosptal wth more decson rghts and control over operatons. We beleve the same may hold true when consderng IT outsourcng and the CMI for at least one reason. The CMI represents an organzatonal, not transacton specfc, measure of complexty of servces offered and supported by hosptals. Lkewse, IT systems generally support organzatonal day-to-day operatons. We hypothesze that a hgh CMI (whch means the average complexty of servces rendered s hgh), the hosptal would prefer to have more control over IT operatons and provde them va n-house producton, other thngs equal. Lkewse, f the CMI s low then we expect a hosptal to outsource for IT servces. Ths 8
assumes that a hgh CMI would requre the use of a more specalzed, unque IT system to manage electronc medcal records and other data flows wthn the hosptal. To avod the possble drect and ndrect costs of hold-up usng outsourcng, a hosptal would prefer more control over IT and that control would be through n-house producton (Table 1). Data and Descrptve Statstcs We model the economc ncentves affectng the decson to outsource for IT servces usng 2004 data from the Amercan Hosptal Assocaton and the use of bnary choce models. Ths data ncludes numerous hosptal-specfc characterstcs such as the organzatonal structure, number of beds, volume of patents, and captal expenses. Also ncluded are estmates for the number of full-tme equvalent (FTE) employees n ITrelated work, and whether ths work was employed nternally or outsourced. Unfortunately, data regardng IT employment or outsourcng were treated as confdental and requred wrtten permsson from ndvdual state hosptal assocatons. For ths study, such permsson was only obtaned from the state of Oklahoma. Thus, the analyss n ths paper s lmted to a sngle state. The data obtaned allows for a full host of econometrc models to be employed, ncludng logt models lookng explctly at the n-house / outsource decson. Table 2 dsplays descrptve statstcs for several characterstcs of hosptals n the study. Approxmately 15 percent of all hosptals choose to outsource ther IT procedures. Several other varables are also of nterest, ncludng the case mx ndex (whch ndcates the relatve 9
severty of the patents beng served), the total number of n-patent days and out-patent vsts, the percentage of all npatents who were Medcare / Medcad recpents, the organzatonal status, and whether or not the hosptal resdes n a non-metropoltan county. Approxmately 49 percent of the hosptals n our study were n non-metropoltan areas, and the two domnant types of hosptals were not-for-profts and those run by the federal government. Methodology A Model for Determnng the Outsource Decson The decson on whether or not to outsource a hosptal s IT servces s a dscrete adopton choce for the hosptal that s dependant on the utlty from outsourcng ( keepng the servces n-house ( ) versus ). The hosptal s utlty wll, n turn, depend on the relatve costs and benefts of the outsourcng versus n-house decson. Therefore, the hosptal wll nvest n outsourcng f otherwse. U 0 U 1 > U 0 * Let y = U U = β ' + ε, 1 0 X U 1, and wll keep the servce n-house where X s a vector of hosptal and place-based characterstcs that nfluence the utlty of outsourcng IT servces relatve to performng them nternally, β ' s the assocated parameter vector, and ε s the assocated error term. Although * y s a latent varable, the presence of outsourcng (meanng y = 1) s observed f y * > 0, and = 0 y otherwse. Hence, Pr( y = 1) = Pr( ε > β ' X ), or Pr( y = 1) = 1 F( β ' X ) 10
Where F ( ) s the cumulatve dstrbuton functon for the error term ε. Each observed y s then a functon of a bnomal process, wth the assocated lkelhood functon expressed as: L = F( β ' X ) [1 F( β ' X y= 0 y= 1 If the cumulatve dstrbuton of ε s logstc, then )] F( β ' X exp( β ' X ) ) = 1+ exp( β ' X ) = 1 (1 + exp( β ' X, and )) [1 F( β ' X exp( β ' X ) )] = (1 + exp( β ' X )) The assocated statstcal model s then estmated by the maxmum lkelhood method, log L = log 1 + exp( β ' X log y= 0 (1 + exp( β ' X ) y= 1 (1 + β ' X ) ) The explanatory varables n matrx X are categorzed nto two dstnct groups, those assocated wth the hosptal and those assocated wth the county n whch the hosptal s located. Results The results from a logstc regresson model are dsplayed n Table 3. Our analyss hghlghts several key factors affectng the use of nternal IT staff versus outsourcng for IT servces: (1) the severty of patent llness (servng as a proxy for transactons costs) postvely mpactng the outsource decson; (2) the counteractng mpacts of n-patent days and out-patent vsts; (3) postve mpacts on outsourcng of governmentally-owned facltes; and (4) the lack of mpact from non-metropoltan status. 11
As Table 3 ndcates, the case mx ndex has a postve mpact on the probablty of outsourcng. Ths mples that hosptals facng ncreased complexty are more lkely to outsource ther IT work, whch runs counter to our ntal hypothess that the two would be negatvely related. Ths may ndcate that our proxy for complexty s a poor measure for the rsk that the hosptal faces when t determnes whether or not to outsource IT servces. Other results suggest that the volume of patents encountered has effects that tend to offset each other: the volume of n-patent days s postvely correlated to the probablty of outsourcng, whle the volume of outpatent vsts s negatvely related. Ths may be an ndcator that the procedures assocated wth n-patent days are easer to predct by the hosptal staff and are therefore easer to move to an off-ste locaton. Although both of these varables are statstcally sgnfcant at the 10 percent level, nether has much economc sgnfcance as evdenced by ther mnmal margnal effects. We also see that, relatve to the base group of for-proft hosptals, an organzatonal structure that s owned by the federal government (ether federal or non-federal) sgnfcantly ncreases the lkelhood of outsourcng. Ths suggests that for-profts are more hestant to allow IT work to be done externally, perhaps due to prvacy concerns or even a percepton that ths actvty s smply not as cost effectve. Fnally, the metropoltan status of the county n whch the hosptal s located does not have a sgnfcant mpact on ts decson to outsource, suggestng that physcal dstance from potental outsourcng stes s not mportant. 12
Table 4 dsplays the correlaton matrx of all ndependent varables used n the regresson analyss. Among all the varables, only one relatonshp s above 60 percent correlaton, whch, perhaps expectedly, s the measure for patent volume: n-patent days and outpatent vsts. However, ths correlaton of 0.734 s not suffcently hgh enough to warrant droppng one of the varables from the analyss, partcularly n lght of the dfferng mpacts these two varables have on the outsource decson. Dscusson Ths paper has provded a methodology for analyzng the IT outsource decson by hosptals by turnng to transacton cost theory. Applyng ths theory suggests that hosptals wth more complcated treatments (proxed n ths study by the case mx ndex) should have a decreased probablty for outsourcng, snce the ncreased complexty may be more problematc for an external frm to handle. Hence, we hypotheszed that ncreased complexty and n-house producton are postvely related. Our results show the opposte - that the case mx s postvely related to outsourcng. From the standpont of transacton cost theory, ths mples that the case mx ndex may not accurately capture the IT related opportunstc rsk faced by a hosptal when procurng IT servces. Instead, other measures such as human and asset specfcty varables may do a better job explanng outsourcng. Further analyss on ths topc s needed to break out the ndvdual roles played by these other varables, suggestng an avenue for further research. 13
Our analyss does turn up several other nterestng results; specfcally the postve relatonshps between governmental organzatonal structures and outsourcng, and the lack of sgnfcance of a non-metropoltan dummy varable. These results may suggest that hosptals operatng for proft stll perceve the outsourcng strategy as too rsky or not cost effectve, or may ndcate that hosptals that are owned by the government have more ncentve to look to outsourcng as a management strategy. The queston of how rural hosptals vew outsourcng s left unanswered by our analyss, but should be further explored gven the ncreasng role of IT n the health sector and global flattenng takng place n today's world. 14
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Table 1. Transacton Cost Theory Hypothess for IT Outsourcng Complexty of Servces Rendered (CMI) Costs of Outsourcng Costs of Employment Predcted IT Arrangement L L H Outsource H H L Employment H - hgh L - low 16
Table 2. Descrptve Statstcs Descrpton Varable Name Mean S.D. Case Mx Index casemxndex 1.268 0.3678 Volume of Patents In-patent days total pdtot 29,099 39794 Total outpatent vsts vtot 70,367 101852 Medcare / Medcad Patents % Medcare npatent medcarepercent 0.512 0.227 % Medcad npatent medcadpercent 0.125 0.118 Organzatonal Status Government - federal govfed 0.025 0.158 Government - nonfederal govnonfed 0.304 0.462 Nongovernmental, not for notforprof 0.430 0.498 proft For Proft 0.241 0.378 Rural Status Nonmetro nonmetro 0.494 0.503 Outsource outsource 0.152 0.3612 Number of Observatons 79 17
Table 3. Logstc Regresson Results Dependent Varable: outsource (0,1) Descrpton Varable Name Coeff S.E. Margnal Effects Case Mx Index casemxndex 4.00080 1.38980 *** 0.17630 Volume of Patents In-patent days total pdtot 0.00003 0.00001 * 0.00000 Total outpatent vsts vtot -0.00001 0.00001 * 0.00000 Medcare / Medcad Patents % Medcare npatent medcarepercent -3.59200 2.59650-0.15830 % Medcad npatent medcadpercent 6.30220 2.87800 *** 0.27780 Organzatonal Status Government - federal govfed 7.49384 2.89900 *** 0.94770 Government - nonfederal govnonfed 4.30027 1.48550 *** 0.47870 Nongovernmental, not for notforprof 1.87413 1.63320 0.10230 proft Rural Status Nonmetro nonmetro 0.31100 0.99060 0.01370 Constant -9.57207 2.62320 *** Pseudo R2 0.39210 Note: *, **, and *** ndcate statstcal dfferences from 0 at the p =.10,.05, and.01 levels, respectvely 18
Table 4. Correlaton Matrx outsource casem xndex pdtot vtot medcare percent medcad percent govfed govnonfed notforprof nonmetro outsource 1 casemxndex 0.197 1 pdtot 0.258 0.446 1 vtot 0.093 0.267 0.734 1 medcarepercent -0.282-0.096-0.133-0.203 1 medcadpercent 0.363 0.055 0.339 0.080-0.247 1 govfed 0.156-0.089-0.082 0.288-0.365-0.171 1 govnonfed 0.104-0.336-0.198-0.181 0.037-0.037-0.107 1 notforprof -0.154-0.055 0.188 0.179 0.249 0.044-0.140-0.574 1 nonmetro -0.065-0.432-0.332-0.215 0.096-0.039 0.002 0.339-0.040 1 19