Drug Prescription Behavior and Decision Support Systems

Transcription

1 Drug Prescrpton Behavor and Decson Support Systems ABSTRACT Adverse drug events plague the outcomes of health care servces. In ths research, we propose a clncal learnng model that ncorporates the use of a decson support system (DSS) n drug prescrptons to mprove physcans decsons about the nal drug selecton and admnstraton. The model allows for both the analytcal nvestgaton of the effects of dfferent DSS features on clncal learnng and the estmaton of the physcan learnng behavor gven a panel data set. The analytcal results suggest that usng a DSS to mprove physcans prescrbng decsons would posvely nfluence ther clncal learnng. Conversely, whout mprovements n successful drug selecton, the use of a DSS would negatvely affect clncal learnng. The emprcal results provde further evdence on the factors that drve physcans responses to nformaton sources and the extent to whch they rely on clncal experence n prescrbng drugs. Key words: Decson support systems, computerzed physcan order entry, adverse drug event, prescrpton error, drug selecton, drug admnstraton, dosage

2 1. INTRODUCTION Researchers estmate that adverse drug events (ADEs) cause between 700,000 and 1.5 mllon njures annually (Carter and Hellng 1992; Leach et al. 1981; Landro 2009; Wllett et al. 1989). A promnent study suggests that 28 percent of the ADEs, most of whch are due to prescrpton errors (Kaushal and Bates 2001; Kohn et al. 2000; Leape et al. 1995), are preventable (Bates et al. 1995). Mrco et al. (2005) fnd that the most common prescrpton errors are defcences related to choosng the rght drug, dosage, frequency, route of admnstraton (.e., plls, gels, lquds), drug nteractons, and length of therapy. The sheer number of prescrpton errors has s roots n the challenges that physcans face n keepng abreast of developments n pharmacology. As powerful new drugs and clncal nformaton become avalable, the need for accurate prescrpton decsons grows proportonately. Thus, defcences n keepng up wh new developments n pharmacology unavodably lead to suboptmal prescrbng decsons, even though the choce and admnstraton of drugs make up some of the most mportant clncal decsons n medcal practce (Soumera et al. 1989). Contnuous physcan learnng s arguably the most effectve soluton to reducng prescrpton errors. Physcan learnng nvolves effectvely ntegratng the clncal experences wh the most recently acqured nformaton and then modfyng the prescrpton behavor accordngly. Physcans regularly update ther belefs and thus learn about the effcacy of drugs from ther own clncal experences (Coscell and Shum 2004). Improvng prescrbng decsons through contnuous learnng would not only mnmze preventable ADEs and provde better treatments for the patents, but also mprove patent satsfacton (Crawford and Shum 2005; 1

3 Dubersten et al. 2007), reduce nsurance rsks, and lead to superor qualy and aud ratngs for the physcans (Ln et al. 2009). When ntegrated wh clncal, practce gudelnes and workflows, decson support systems (DSSs) and computerzed physcan order entry (CPOE) can help physcans wh ther clncal learnng and thus enhance ther prescrpton decsons. CPOE refers to computerzed systems that automate the medcaton orderng process. Basc CPOE features nclude verfcaton of typed orders n a standard and complete format, and CPOE systems typcally have or nterface wh DSSs of varyng sophstcaton, although some DSSs are mplemented whout a CPOE (Kaushal and Bates 2001). In general, CPOE and DSSs support two types of decsons: drug selecton and drug admnstraton. Drug selecton refers to the nal decson of matchng a patent wh an approprate drug from a set of alternatves. Computerzed decson support on drug selecton s provded through drug recommendatons, drug allergy checks, drug laboratory value checks, and drug drug nteracton checks. Drug admnstraton refers to how the selected drug should be admnstered n terms of dosage, frequency, route, and length of therapy, and such decsons are supported wh approprate recommendatons by the software.the drug selecton feature of CPOE has been shown to reduce the rate of non-ntercepted, serous prescrpton errors by more than half (Bates et al. 1998; Bates et.al 1999). The use of DSSs has also been shown to reduce the errors assocated wh drug admnstraton (.e., decsons regardng medcaton dosage, frequency, and route). Table 1 summarzes the lerature on the effect of DSS use on prescrbng decsons and outcomes. [Insert Table 1 here.] 2

4 Because DSSs do not replace physcan judgment 1, the sustanable posve results can be acheved only through mproved physcan learnng supported wh DSSs. Bochccho et al. (2006) also argue that the man benef of computerzed decson support s smply mproved pharmacologcal knowledge. Physcans assume full responsbly of ther prescrbng decsons wh or whout usng a DSS, and therefore the most successful DSSs are those that best faclate physcan learnng. Our objectve n ths paper s to understand the nteracton between physcan learnng and the use of a DSS and the correspondng mpact on prescrpton decsons. We also am to understand whch type of decson support s more crcal for physcan learnng. To ths end, we develop a model of physcan prescrpton behavor supported by two types of DSS features. One category of DSS features supports the decsons regardng when to prescrbe a focal (drug selecton), and the other category supports the drug admnstraton decsons for the focal drug. Usng the DSS features can potentally reduce the varances and uncertantes behnd drug selecton and admnstraton decsons and nfluence physcans learnng, wh the objectve that prescrpton behavors are n lne wh the clncal gudelnes establshed for the focal drug. The proposed framework provdes both an analytcal model to nvestgate the effects of these two DSS capables and an emprcal model to estmate the physcan prescrpton behavor gven a panel data set (for other smlar emprcal models, see Akçura et al. 2004; Coscell and Shum 2004; Erdem and Keane 1996; Kohn et al. 2000). The model accounts for the followng two factors: (1) physcans may be subject to dfferent patent profles and experences, and (2) they may arrve at dfferent clncal conclusons, even after observng the same evdence, because of ther pror clncal experences (Kohn et al. 2000). 1 For example, Burke and Pestotnk (1999) fnd that physcans prescrbe the computer-suggested antbotcs only approxmately 46 percent of the tme. 3

5 Usng the proposed model, we ask the followng research questons: How are the two types of DSS features related to physcans clncal learnng about a focal drug? What are the salent physcan characterstcs that affect clncal learnng? What are some of the mportant physcan-level factors that faclate the adopton of DSSs? We use a herarchcal Bayesan estmaton technque that captures the ndvdual, physcan-level uncertantes and learnng behavor. Thus, the proposed model can be used to analyze, compare, and contrast dfferent physcan responses to the use of computerzed decson support n the prescrpton process. Prevous research n nformaton systems has shown the mportance of combnng ndvduallevel learnng behavor and user envronment (Ives et al. 1980). A contrbuton of ths study s that combnes physcan learnng and the use of nformaton technology n modelng physcan behavor. The analytc modelng approach combned wh the emprcal analyss of clncal learnng behavor provdes a powerful framework for capturng the mpact of DSS on physcan learnng. The analytcal results emphasze the mportance of computerzed support for drug selecton decsons and hghlght both the benefs and the rsks assocated wh desgnng and mplementng DSSs. When DSSs lead to superor drug selecton decsons, patent-level observatons are better ntegrated nto the prescrpton behavor, whch mproves physcan learnng. An mplcaton of ths result s that proper desgn and use of DSS may help n enforcng complance wh treatment protocols and reducng prescrpton errors. Thus, the model provdes an explanaton on when and how the use of a DSS would allow us to observe physcan decsons smlar to those of an expert panel (Shortell et al. 1998). We also fnd that, whout mprovements n the accuracy of drug selecton decsons, the use of a DSS negatvely nfluences physcans clncal learnng because they attrbute less mportance to the nformaton they gather 4

6 from patents than to ther establshed expectatons of the drug. Consequently, mproper desgn and mplementaton may lead to negatve outcomes (Coscell 2000; Leape et al. 1995). The emprcal results provde further evdence on the role of the nformaton acqured through clncal experence. We fnd that physcans dffer substantally wh regard to ther responses toward the nformaton sources and clncal experences. Physcan specalty and locaton have sgnfcant effects on the overall physcan responses to new nformaton about a focal drug. General practce physcans (.e., generalsts) and physcans located n hgh-ncome areas rely more on ther clncal experences than specalsts and physcans located n lowncome areas, respectvely. Accordngly, our analyss suggests that computerzed decson support for drug selecton benefs specalsts and physcans located n low-ncome areas relatvely more. These results provde further evdence on the mportance of specalty and locaton on the success of DSS use. We organze the rest of the paper as follows: We frst present an analytcal model that captures the physcan prescrpton and learnng behavor n Secton 2. Then, we descrbe our data and emprcal methods n Secton 3. The emprcal results on salent physcan characterstcs and how they are related to DSS usage are then presented n Secton 4. The paper concludes wh a summary and dscusson n Secton MODEL We begn wh a basc model that formulates physcans prescrpton of a focal drug n the absence of DSS and clncal learnng. We extend the model frst wh two DSS features and then wh a mechansm for clncal learnng about the focal drug. Fnally, we present the analytcal results on how the two DSS features faclate physcan learnng. 5

7 2.1 Basc Model Consder, for example, patents who suffer from exstng condons that requre ongong treatments. Bpolar dsorders or cardovascular dseases are examples of such condons. Physcans consder prescrbng a focal drug n treatng ther patents gven the exstng condon. Physcans also prescrbe the focal drug accordng to ther preferences, past habs, and external nformaton sources about the drug (Soumera 1989). Physcans dffer dependng on the profle of ther patents, ther prescrpton habs, and ther responses to the external nformaton they receve about the drugs. Physcan uncertantes may arse n prescrbng the focal drug to the rght set of patents wh the correct dosage, frequency, route, and length of therapy. Physcans face uncertanty n decdng whether the focal drug s the most approprate one for a specfc patent. In makng ths decson, each physcan consders the characterstcs of the drug, ncludng s sde effects n vew of a patent s general health and lfestyle. For example, certan drugs that reduce cholesterol levels should not be prescrbed to patents wh pre-exstng health condons. A smlar concern exsts for patents who suffer from bpolar dsorders because they often have a multude of medcal problems and therefore need to take addonal drugs that may nteract wh the drugs used to treat bpolar dsorder. For example, chldren wh bpolar dsorders have a hgh rsk of developng attenton defc hyperactvy dsorder (ADHD), and the stmulants used to treat ADHD can complcate the bpolar treatment. ( Moreover, some patents may be allergc to certan drugs, some others may experence sde effects due to ther lfestyles, or the focal drug may not perform as expected, leadng to unforeseen reactons. Chan and Hamlton (1996) show that even the least effectve drug may stll have a sgnfcant market share 6

8 because of the heterogeney n the effectveness of drugs and ther sde effects on patents. Thus, physcans consder selectng the focal drug wh varyng degrees of confdence. Let the random term ω represent the overall drug selecton uncertanty for physcan at tme t. We vew ths uncertanty as physcan specfc accordng to physcan actvy learnng theory (Engeström 2001). Ths theory poss that the most approprate un of analyss s the subject (n our case, a physcan) who learns how to carry out a meanngful actvy (.e., prescrbng the focal drug gven the exstng condon) n a system of actves (.e., through many nteractons wh the patents) (Jonassen and Murphy 1999). The dosyncrases n physcans patent profles contrbute to the dfferences n physcan-specfc characterstcs, whch n turn generate physcan-specfc uncertantes when selectng the drug. Let Q represent physcan s preference for the focal drug gven the exstng condon among the patents, where hgher values of Q represent a more posve preference for the focal drug. The number of prescrptons wrten n perod t s a functon of physcan s preference for the focal drug Q and the overall drug selecton uncertanty ω. Assume that durng perod t, physcan handles n new patents. Let Y denote the total number of new prescrptons n perod t and follow a Posson dstrbuton. The probably of observng y prescrptons equals the followng: p( Y = y y µ e ) = y! y, (1) 7

9 where the mean of the dstrbuton µ s proportonal to 2 n exp( Q + ω ). (2) External nformaton sources help physcans establsh a preference for the focal drug (Soumera et al. 1989; van den Bulte and Llen 2001). We consder three external nformaton sources avalable to physcans (Mzk and Jacobson 2004). Frst, face-to-face dscussons and tranng materals on the focal drug are a sgnfcant source of nformaton for physcans. Usng the ndustry termnology, we call these actves detalng. Second, drug samples provde a tral opportuny for physcans and act as an addonal nformaton source that may nfluence a doctor s preference. We call the clncal use of drug samples samplng. Fnally, conferences, publcatons, advertsements, and other announcements represent the thrd external nformaton source. Because ths nformaton source usually pertans to alternatve drugs and treatments other than the focal drug, we refer to ths as competve actves. Gven the exstng medcal condons of the patents, physcan s preference for the focal drug Q s modeled as the followng: Q = GQ β + detalng β amplng + s β omp+ νc +, (2),t where detalng, s amplng, and c omp are the detalng, samplng, and competve actves that physcan experences about the focal drug durng perod t and β 1, β 2, and β 3 are the correspondng response coeffcents assocated wh these actves. Accordng to Equaton (2), 2 Note that a change n Q alters the probably of prescrbng the focal drug ( µ / n ) and accounts for n. The term Q takes a hgh value f the drug works all the tme for all patents n the therapeutc category. In contrast, a low value of Q reduces physcans probably of prescrbng the focal drug. 8

10 posve response coeffcents for samplng, detalng, and competve actves renforce a physcan s preference for the focal drug. The physcan preference ( Q ) s a functon of past preference (, 1) Q carred over to the next perod subject to a coeffcent G, whch s a physcan-specfc decay coeffcent that captures the mpact of a physcan s habs (past belefs and preferences) on the current preference for the focal drug. Accordng to pror research, physcan behavor s drven to a large extent by past belefs (Coscell 2000; Davs et al. 1995; Dubersten 2007; Fscella et al. 2000). Fscella et al. (2000) argue that rsk-averse physcans are more lkely to dscount ther pror belefs and seek addonal nformaton. Davs et al. (1995) suggest that experenced physcans are more lkely to beleve that they already operate at or near optmum levels (whch they term celng effects ). That s, what physcans already know plays a major role n shapng ther future behavor. Because physcans also exhb dfferences n ther cognve flexbly to assmlate new nformaton, they dffer n terms of how long they preserve ther old habs n lght of new nformaton. In our model, a physcan wh a hgh carryover coeffcent G (closer to one) tends to repeat prevous prescrpton choces frequently. In general, physcans wh a hgh G / β rato are more lkely to exhb celng effects. A hgh G. suggests that physcan reles heavly on past habs, and a low β represents a low response to new nformaton. The term ν n Equaton (2) represents the drug admnstraton uncertanty, whch s physcan s uncertanty about how to admnster the focal drug after decdng to prescrbe to the patents n perod t. The effectveness of the focal drug s closely related to s correct use, whch requres dentfyng the correct dosage, frequency, route, and length of therapy. For example, statn drugs are frequently used to treat cardovascular problems. They come n varous 9

11 dosages from as low as 5 mllgrams to 80 mllgrams wh varous usage frequences. Wrong dosage and frequency decsons present sgnfcant health rsks. For example, a wrong drug dosage when treatng bpolar dsorders leads to sgnfcant complcatons and sde effects, such as thyrod problems, stomach pan, drowsness, and memory and concentraton dffcultes that may even lead to sucde and loss of lfe. 3 Smlar to the drug selecton uncertanty ω, the drug admnstraton uncertanty ν s also physcan specfc. 2.2 Impact of DSS As dscussed prevously, the use of DSSs can mprove drug selecton and admnstraton decsons. Recall that physcans face varous degrees of uncertantes when selectng ( ω ) and admnsterng ( ν ) the focal drug. The effectveness of decson support for drug selecton and admnstraton vares dependng on the sophstcaton of the DSS features. The effectveness of the DSS n reducng the drug selecton uncertanty s captured byγ, where 0 < γ < 1. Let the drug selecton uncertanty ω follow a normal dstrbuton wh mean zero and varance γ, where W > 0. Physcans rely on ther own professonal knowledge n W the absence of a DSS. Ths s the case n whch γ equals one and the uncertanty s captured by the physcan-specfc varance W. Conversely, a low γ value ndcates that the DSS s effectve n dentfyng the most approprate drug and recognzng mportant and relevant sde effects gven the patent profle. Ths s equvalent to usng an advanced DSS that makes approprate drug recommendatons as well as executes all the necessary checks, such as drug allerges and drug drug nteractons. 3 See, for example, /CL

12 The effectveness of the DSS n reducng the drug admnstraton uncertanty s captured byδ. Smlar to the precedng dscusson, we let the dosage uncertanty ν follow a normal dstrbuton wh N 0, δ V ) where, 0 < δ < 1 and V > 0. As δ decreases toward zero, the DSS ( gets better at dentfyng and mnmzng errors related wh the admnstraton of the focal drug. An ntermedate δ value can be vewed as usng a DSS that s effectve n recommendng the approprate drug dosage and frequency, but perhaps not so effectve n suggestng the route and length of therapy when treatng the exstng condon wh the focal drug. A DSS wh a lowδ value would make approprate recommendatons n all aspects of drug admnstraton. Note that consstent wh Dresel and Bnder s (2005) evdence, a physcan wh a hgh degree of uncertanty ( V h, W h ) n our settng benefs more from the use of a DSS than a physcan wh a low degree of uncertanty ( V l, W l ), gven ( 1 δ )( V h V ) > 0 and ( 1 γ )( W h W ) > 0. Drug selecton and drug admnstraton uncertantes vary at the physcan l level to allow each physcan to benef dfferently from computerzed decson support on drug selecton and drug admnstraton (Pearson et al. 2009). l 2.3 Clncal Learnng about the Focal Drug So far, the process has captured prescrpton behavor ndependent of the clncal learnng about the focal drug that occurs through the observaton of the clncal outcomes. In the basc model presented n Equaton (2), the physcans use the external nformaton sources but not the nformaton obtaned through the clncal experence. In realy, each physcan lkely develops an ntrnsc preference about a drug based on the clncal experence wh the patents. Physcans observe ther patents whle searchng for the rght drugs and dosages to mprove treatment outcomes and elmnate potental problems (Shortell et al. 1998). For example, f a physcan 11

13 dscovers that the focal drug beng used for the treatment of the exstng condon s leadng to certan adverse sde effects among the patents, the physcan wll lkely revse hs or her preference for the drug for future reference. Alternatvely, a repeated posve experence wh a focal drug may lead a physcan to prescrbe the drug even more frequently. In what follows, we propose a mechansm that captures the role of a DSS as well as other salent factors on physcan-level learnng. In the model, physcans vary n terms of the benef they derve from computerzed decson support, data analyss, and nterpretaton (Barnes 1998; Dresel and Bnder 2005). Physcans ncorporate ther clncal experences nto ther prescrpton preferences usng a Bayesan updatng rule. The use of Bayesan updatng has been advocated n modelng physcan learnng (Lndgaard et al. 2009). Physcans start each perod wh a pror preference for the focal drug. Usng the clncal experence n a gven perod, each physcan updates the pror preference to form a posteror preference, a process that s repeated every perod. Let physcan s pror preference at tme t be denoted by Q, t t 1. The ndex t t 1 ndcates that the updatng process nvolves the clncal nformaton gathered untl the end of perod t 1 but excludes the clncal nformaton obtaned n perod t. Here, Q, t t 1 s a functon of physcan s posteror preferences at the end of perod t 1 (, 1 t 1 ) Q. Consder the end of perod t 1 when the physcans ncorporate all the nformaton and establsh the posteror preference Q, t 1 t 1. Let Q, t 1 t 1 follow a normal dstrbuton wh N (, t 1 t 1 M, R, 1 1 ). Then, the mean and varance of physcan s pror preference at the start of t t perod t, Q, t t 1, are gven by the followng: 12

14 M β= GM + β detalng amplng + β s omp + c, (3), t t 1, t 1 t R 2 1 = G R, t 1 t 1 + δ. (4), t t V Accordng to Equaton (3), the mean of the pror preference M, t t 1 depends on the mean of the posteror preference M, t 1 t 1, as well as nformaton sgnals from the most recent nformaton ( detalng, s amplng, and c omp ). The uncertanty assocated wh the pror preference s reflected n the expresson for R, t t 1 n Equaton (4). Equatons (3) and (4) together capture all the nformaton a physcan receves, wh the excepton of the mpact of the most recent clncal experence and learnng. The term Q, t t denotes the posteror preference updated after the observaton of the clncal experence by the physcan durng perod t. Let φ represent the observed outcome at the end of ths perod. Then, the dstrbuton of the posteror s gven by Q, t t ~ N( t t where M,, R t t, ), M M + K ( φ M 1), (5), t t =, t t 1, t t R =, t t R, t t 1 K R, t t 1, (6) and K s the clncal learnng coeffcent for physcan durng perod t. The term K captures the extent to whch clncal observatons are ntegrated wh physcan s preference for the focal drug n perod t, and we defne K as follows: K = R R, t t 1. (7) + γw, t t 1 13

15 We obtan Equatons (5), (6), and (7) usng the Kalman flterng technque, whch requres margnalzng a jont normal dstrbuton. Kalman flterng s commonly employed n the ndvdual (patent) learnng lerature (Akçura et al. 2004; Coscell and Shum 2004; Erdem and Keane 1996). Equatons (1) and (2) are jontly normal. Equatons (5), (6) and (7) are derved by margnalzng the jont dstrbuton gven the prescrpton observaton φ. 2.4 An Analyss of the Physcan Clncal Learnng Equatons (5), (6), and (7) jontly represent the clncal learnng mechansm. Frst, we dscuss how these equatons represent the clncal learnng behavor of physcans. Second, we nvestgate how the two types of DSS features nteract wh the clncal learnng behavor. The term φ ) n Equaton (5) represents the nformaton dscrepancy between ( M, t t 1 the observed outcome φ at the end of perod t and the mean value of pror preference (see Equaton 3) M, t t 1 at the start of perod t. The change n posteror mean M, t t n Equaton (5) depends on the sgn of ths dscrepancy. For example, many faled therapes across the patent profle usng the focal drug are equvalent to a negatve φ ), whch gets ncorporated ( M, t t 1 nto the future preference for the focal drug as part of the learnng process. Such a clncal observaton reduces the posteror mean M t t, accordng to Equaton (5). Equaton (6) shows that the posteror varance drops as new nformaton s acqured. Over tme, physcans gan clncal experence and reduce ther uncertantes on the drug s performance and s f to ther patents. Note that the posteror uncertanty ( R t t, ) decreases n proporton to the pror uncertanty ( R, t t 1 ). Because of the dynamc nature of the process, the nformaton obtaned early on (under a relatvely hgh level of uncertanty) affects physcan 14

16 confdence more than the nformaton obtaned later (when the degree of uncertanty s relatvely lower). We also observe n Equatons (5) and (6) that the extent to whch a physcan reles on new clncal experence n updatng the preference s determned by the value of the learnng coeffcent K. The learnng coeffcent represents the weght attached by physcan to the nformaton sgnals receved through clncal experence when ntegratng the new nformaton. The term K ranges from zero to one dependng on the uncertanty levels ( R, t t 1 and γ W ), as specfed by Equaton (7). Note that K drops as physcans reduce ther uncertanty wh clncal experence (see Equaton 7), lmng the role of new nformaton n the updatng process. The mpact of the DSS features on clncal learnng vares for each physcan (Barnes 1998; Dresel and Bnder 2005) through the physcan-specfc K. Fgure 2a llustrates the benchmark case for a gven physcan wh no mpact of clncal experence. The horzontal axs represents the perod, and the vertcal axs represents the focal drug prescrptons. The sold lne represents the prescrpton level based on clncal gudelnes, whch ncorporate the relevant complances and agreed-on treatment protocols by the experts. Note that n Fgure 2a the physcan prescrbes the focal drug at a strctly lower rate than what an expert panel would, as the dotted curve tled Physcan Prescrpton Preference ndcates. Because the physcan does not learn from clncal observatons, the physcan prescrptons represented by the dotted curve contnue to reman well below the prescrptons based on the clncal gudelnes. The dfference between the two s represented by the long dashed curve, whch should trend lower f the physcan were to benef from the nformaton ganed through clncal experence. 15

17 In Fgure 2b, we provde a smlar graph that shows the trend of the prescrptons across tme, but n ths case wh an actve learnng mechansm. Contrary to the case wh no clncal learnng, the dfference between the actual preference and the prescrpton level based on clncal gudelnes eventually dsappears over tme, and the long dashed curve approaches zero. [Insert Fgure 2 here.] If a physcan effectvely uses new nformaton for learnng, the dfference n Fgure 1b s quckly mnmzed, and thereafter the physcan prescrptons closely follow the prescrptons based on clncal gudelnes. Ths s the case n whch the K coeffcent n Equaton (7) s hgh, suggestng a sgnfcant level of Bayesan updatng through Equaton (5) and, thus, the quck approach of the long dashed curve to zero n Fgure 1b. Recall that K depends on the drug selecton and admnstraton uncertantes (δv and γw, see Equaton 7) and that DSS use affects the clncal learnng coeffcent K through δ and γ. We defne the steady-state learnng coeffcent usng Equaton (7) as K δv δv + γw =, where 2 V = V /(1 G ). (Note that R, t t = R, t t 1 = R at the steady state. Usng Equaton 4, we obtan R 2 = δ V /(1 G ). We derve ths expresson by replacng R, t t 1 wh R n Equaton 7.) The dervatves of K wh respect to δ and γ are as follows: dk = dδ γv W ( δv + γw ) 2 > 0, and (8) dk dγ = δv W ( δv + γw ) 2 < 0. (9) 16

18 The steady-state clncal learnng coeffcent K decreases wh the DSS s drug admnstraton effectveness (accordng to Equaton 8) and ncreases wh drug admnstraton effectveness (accordng to Equaton 9). Consderng that a hgh K value represents a fast pace of learnng, Equatons (8) and (9) together suggest that a DSS can contrbute to clncal learnng only f reduces the drug selecton uncertanty (low γ). Otherwse, learnng does not mprove even f the DSS reduces the drug admnstraton uncertanty (low δ). We formalze ths n the followng proposon: Proposon 1. Usng a DSS does not posvely contrbute to physcan learnng, even f reduces the drug admnstraton uncertanty, unless also reduces the drug selecton uncertanty. Proposon 1 provdes new nsghts wh mplcatons on how and when to use DSSs for prescrpton purposes. Consder a physcan who compares the nal preference for a focal drug wh the observed prescrpton outcome assocated wh the use of the drug and dentfes the dscrepancy between the two. Such a mental exercse would enable the physcan to decde how to modfy future prescrptons dependng on the weght he or she assgns to the clncal observaton. When a physcan experences a hgh level of drug selecton uncertanty, and the patent drug match s n doubt (.e., hgh γ ), the clncal experence that takes place n perod t has a lmed effect on the preference updatng process. Note that K decreases wh γ (see Equaton 9). Drug nteractons and dfferent patent lfestyles may prevent physcans from observng all the relevant nformaton and, n turn, lm ther ably to update ther preferences for the focal drug (low K ). When the drug selecton uncertanty s hgh, a physcan wll not be 17

19 able to accurately nfer the success of hs or her admnstraton of the focal drug. Consequently, clncal learnng and the level of change n future prescrpton behavor wll be lmed. An mplcaton of Proposon 1 s that seasoned physcans who prefer to operate accordng to ther past habs do not constute an approprate target populaton n terms of DSS adopton. Experenced physcans may prefer to follow the suggestons of a DSS only after selectng the drug or may not pay any attenton to the DSS at all (Dresel and Bnder 2005; Pearson et al. 2009). In such cases, gven the DSS s lmed mpact on the drug selecton decson (.e., γ s close to one), the overall mpact of the DSS on physcan learnng wll also be lmed. The physcans may even vew the DSS as a nusance and hassle. Even f the DSS makes relevant recommendatons on drug admnstraton, the physcans are not lkely to obtan any long-term benef because of the lmed learnng. A few other studes have also reported that the use of DSS may not mprove prescrpton outcomes and may even be perceved as counter-productve by physcans. In the context of health care, Lndgaard et al. (2009, p. 526) examne the mportance of the dagnostcy of nformaton, whch they and Wells and Lndsay (1980, p. 778) defne as how much mpact a datum should have n revsng one s opnon on an ssue whout regard to what the pror odds are. They show that when the dagnostcy of the new nformaton provded by the DSS s low, physcans do not nternalze the nformaton and make based decsons. In our context, low dagnostcy (and, thus, lmed learnng) occurs when the DSS fals to provde relable nformaton that boosts physcans confdence n the approprateness of the focal drug for the patents. Lerch and Harter (2001) observe through experments that may be dffcult to mprove learnng wh a DSS under dynamc, real-tme envronments. Furthermore, they fnd that certan types of cognve support can degrade decson makers performance n the presence 18

20 of tme pressure. In a smlar ven, Wllams et al. (2007) examne the effect of DSS use on decson makers error patterns and decson qualy. They fnd that the accdental effects (e.g., mechancal errors) ntroduced by a DSS may lead to lower-qualy decsons and thus defeat the purpose of usng the system. Coera et al. (2006) fnd that some physcans may comm new types of error bases (e.g., automaton) because of usng a DSS, n whch decson makers mss mportant nformaton because the system does not prompt them. They may also comm errors of commsson, n whch they do what the decson ad tells them to do even when ths contradcts ther tranng and other avalable data (Chsmar and Patton 2003). Thus, the evdence suggests that n some cases, DSS use may ncrease the number of decson errors or ntroduce new types of errors. Accordng to Proposon 1, the best approach to prevent such negatve, unntended outcomes s to ensure that the use of the DSS s assocated wh reduced drug selecton uncertanty. So far, we have consdered the mpact of DSS use on drug selecton and admnstraton decsons separately; we now analyze the mpact of a smultaneous mprovement n the two types of decsons due to the DSS. Consder an advanced DSS that reduces both drug selecton and drug admnstraton uncertantes, and let the capables of the DSS be represented by the parameters γ 1 and δ 1. Let the capables of a less advanced DSS be represented wh γ and δ, where γ 1 < γ and δ 1 < δ. In addon, let K 1 denote the learnng coeffcent for the more advanced DSS. For ths system to mprove clncal learnng beyond what can be acheved wh the less δ1v δv advanced DSS, we need K 1 K > 0, or equvalently, > 0 δ V + γ W δv + γw 1 1. Ths γ γ1 δ δ1 condon holds f and only f / > 1, that s, when the mprovement n the drug γ δ selecton uncertanty s elastc wh respect to the mprovement n the drug admnstraton 19

21 uncertanty. Thus, swchng to the more advanced DSS s benefcal from a learnng perspectve only f makes physcans more confdent about ther drug selecton decsons. Ths leads to our next proposon. Proposon 2. Consder a DSS (represented wh parameters γ 1 and δ 1 ) that reduces drug selecton and drug admnstraton uncertantes more than another (less advanced) DSS (represented wh parameters γ and δ), where γ 1 < γ and δ 1 < δ. The more advanced DSS faclates the clncal learnng more than the less advanced DSS f and only f the change n the physcan s drug selecton uncertanty s elastc wh respect to the change n drug γ γ1 δ δ1 admnstraton uncertanty such that / > 1. γ δ Proposon 2 provdes the condon whch ensures that the mprovements n decson support contrbute to long-term mprovements n physcan behavor. In other words, ths condon suggests that the reducton n the drug selecton uncertanty due to the mprovements n decson support should be more than the reducton n the drug admnstraton uncertanty. Proposon 2 further emphaszes the noton that not all DSS mprovements benef physcans. DSS capables should ensure that physcan confdence n the drug choce s renforced (e.g., by mprovng the decson support on drug drug nteractons and other sde effects). Whout such an mprovement, physcan learnng wll not be faclated even wh more advanced DSS capables. In lne wh Lndgaard et al. s (2009) termnology, an mplcaton of Proposons 2 s that mprovements n DSS capables should am to ncrease the dagnostcy of new nformaton for the physcans. Better dagnostcy through mproved DSS features requres reducng the drug selecton uncertanty more than the drug admnstraton uncertanty. Another mplcaton s 20

22 that the dentfcaton of the rght set of physcans and use occasons (whch exhb a hgh degree of nal drug selecton uncertanty) s crcal from an adopton perspectve. For example, Mkulch et al. (2001) report that physcans who specalze n pedatrc fever and low back pan do not prefer to use a DSS, whereas physcans who specalze n occupatonal exposure to blood or body flud always use a DSS. Ths suggests that physcans benef from computerzed decson support relatvely more when dealng wh complex ssues that nvolve a hgh degree of uncertanty on the nal treatment selecton. Fgure 3 llustrates the dscrepancy between the physcans prescrptons of the focal drug and the prescrptons based on clncal gudelnes under varous DSS scenaros. Recall that the DSS has no mpact on physcans prescrbng behavor when δ = 1 and γ = 1. The long dashed curve n the fgure represents the base case wh no DSS. The dotted curve represents the case n whch the DSS effectvely supports the drug admnstraton decsons only (δ < 1 and γ = 1). Although the dotted curve trends toward zero, the trend s slower than that n the baselne case wh no DSS (see the dotted curve versus the long dashed curve n Fgure 3). Thus, the dscrepancy between physcan prescrptons and the prescrptons based on clncal gudelnes s hgher than s n the base case. In other words, mprovng only drug admnstraton decson support s not effectve, because n ths case, physcans dscount ther clncal observatons relatvely more. Reducng the selecton uncertanty (γ < 1) through computerzed decson support enables physcans to better extract valuable nformaton from ther clncal observatons and ntegrate effcently wh ther overall treatment preferences. In turn, ths results n the relatvely quck elmnaton of the dscrepancy between actual physcan behavor and best 21

23 practces. The dscrepancy s mnmzed when the DSS effectvely reduces both types of uncertantes (see the sold lne n Fgure 3). [Insert Fgure 3 here.] 3. DATA A large pharmaceutcal company (herenafter referred to as the focal frm ) n the Uned States provded the data set, whch ncludes ndvdual physcan prescrpton records n a therapeutc category. The focal frm produces and markets one (focal) drug n the category; there are no generc alternatves. The annual combned sales of all the drugs n the category are $4.1 bllon. The patents who requre treatment n ths category suffer from chronc dseases. The data nclude the number of new prescrptons wrten by each physcan n the sample durng each month between 2001 and The data also nclude the number of detals (vss by sales representatves) and the number of drug samples receved by each physcan per month. Furthermore, the data contan nformaton on each physcan s specalty and locaton by zp code. General practce physcans, whom we also refer to as generalsts, are dstngushed from specalsts who possess expertse n the therapeutc area. Our sample ncludes a natonwde representatve sample of 1,000 physcans. The physcans n the sample wrote prescrptons each month and were detaled at least three tmes durng the observaton perod. We augmented the data made avalable by the pharmaceutcal frm wh secondary data on per-capa ncome and urbancy ndex of each zp code n whch the physcans n our sample are located. Table 2 presents a summary of the descrptve statstcs. [Insert Table 2 here.] The focal drug was launched n The chemcal formulaton behnd the drug was frst approved for the preventon of a condon and was later extended to the treatment of the 22

24 same condon two years after s ntroducton to the market. The focal drug s the fourth-most popular drug n the market wh a 14 percent market share. The frst-, second-, and thrd-most popular drugs had average market shares of 32 percent, 18 percent, and 15 percent, respectvely, durng the observaton perod. The combned sales volume of all the drugs n the category and the market shares were farly stable durng ths perod. Altogether, there were nne dfferent drugs n the category. Although these drugs treat the same condon, each drug requres a dfferent treatment plan wh dfferent sde effects, speed of onset, and length of therapy. 3.1 Model Specfcaton We operatonalze the varables for the external nformaton sources n Equaton (2) as follows: Detalng s represented by the varable detalng, whch denotes the monthly detalng effort drected at physcan by the focal frm durng perod t. We use the natural logarhm of detalng to adjust for dmnshng margnal returns (Boehm et al. 2001). The second varable samplng denotes the focal frm s samplng actvy drected at physcan durng perod t. Often, the drug representatves hand out samples to physcans durng ther sales vss. However, we also observe a few cases n our data n whch samplng takes place n the absence of detalng actves. It s lkely that some samples may have been delvered by mal or dropped off n the offces. Note that the correlaton coeffcent between detalng and samplng s Because heavy samplng usually accompanes detalng, we normalze the number of samples by the detals receved by physcan n the same perod. A natural logarhm transformaton accounts for the dmnshng margnal returns for the samplng effort. We expect the sgns of both the detalng coeffcent, β 1, and the samplng coeffcent, β 2, to be posve (Gönül 2001). The fnal varable n Equaton (2) s the competve marketng actves that represent other treatments and drugs. Informaton obtaned on competng treatments s lkely to play a role n 23

25 nfluencng physcans preferences for the focal drug. In lne wh the work of Mzk and Jacobson (2004), we use the number of prescrptons for closely competng treatments as a proxy for ther competve detalng and samplng actves. To remove the effects of drug-specfc fxed factors that account for the baselne demand, followng Bouldng and Staeln 1990, we take the frst dfference of the prescrptons of the man competng drugs and dvde by the total category prescrpton volume to construct the thrd varable comp. We expect the sgn of the estmate for the correspondng coeffcent to be negatve. n β and We use the observable physcan characterstcs to explan the unobserved heterogeney G n Equaton (2). Specfcally, we have the followng: β = Θ 1 +, (10) Z τ G = Z 2 + λ, (11) where τ and λ follow..d. normal dstrbutons wh N(0, T) and N(0, Λ). We use three demographc varables to construct both Z 1 and Z 2 varable vectors: the physcan s specalty, per capa ncome, and urbancy of the area n whch a physcan s located. We mean-center all the aforementoned demographc varables. Fnally, note that equaton (1) provdes a real number, and the number of prescrptons requres an nteger. We use a Posson dstrbuton wh the mean gven n Equaton (1) to obtan the number of prescrptons. See the detals n the Appendx on how Equaton (1) determnes the number of prescrptons wrten by physcan durng perod t. 3.2 Model Estmaton We estmate the model usng a herarchcal Bayesan technque that nvolves a Markov chan Monte Carlo (MCMC) smulaton. The MCMC technques are powerful n capturng unobserved heterogeney at the ndvdual level (Ross and Allenby 2003). The essental 24

26 approach s to eratvely sample from the margnalzed posteror dstrbutons, gven all the other parameters, untl the estmates of the model parameters reach a steady state across successve samples. The samples obtaned n ths steady state provde a sample from the jont dstrbuton of the parameters. We use the Metropols algorhm to draw from the posteror denses when dstrbutons do not represent conjugate pars. We use the Gbbs sampler to obtan the draws for the rest of the parameters (Chertow et al. 2001). To demonstrate that the estmaton procedure can ndeed recover the true values of parameters, we estmate the model usng smulated data. Overall, the smulaton results reveal that the estmaton procedure can successfully recover the true parameter values. The estmaton procedure follows the establshed steps n the prevous lerature (see, e.g., the appendx n Allenby and Lenk 1994). (The exact detals of the estmaton are avalable on request.) 4. EMPIRICAL RESULTS Table 3 presents the estmates. We observe that the mean response for the detalng parameter ( β 1) s posve and statstcally sgnfcant and that all physcans n our sample have a posve response coeffcent. Ths ndcates that the physcans use the detalng by the pharmaceutcal company as a sgnfcant source of nformaton regardng the drug s effcacy. As Table 3 shows, detalng efforts tend to have a stronger effect on specalsts than on general practce physcans. Ths may be because specalsts are more actvely engaged n learnng about new research fndngs than general practce physcans. The mean estmate for the samplng varable coeffcent ( β 2 ) s posve and statstcally sgnfcant. Ths reveals that samplng plays a sgnfcant role n helpng physcans match patents wh drugs (and thus helps them update ther preferences). We also observe n Table 3 that the mean estmate for the competve actves 25

27 (β 3 ) s negatve and statstcally sgnfcant. As expected, ths suggests that such competve actves have a negatve mpact on physcans preferences for the focal drug. The mean estmate of the carryover coeffcent G s Overall, ndvdual physcans show a relatvely dspersed pattern of persstence n ther preferences, wh 15 percent of them havng carryover coeffcent estmates less than 0.5. Ths suggests that whle a majory of the physcans have farly stable preferences toward the drug, some physcans adjust ther preferences relatvely more over tme. An examnaton of the profles of physcans wh low estmates of G reveals that specalsts tend to have a sgnfcantly lower degree of carryover coeffcents n ther preferences than general practce physcans. Ths ndcates that specalsts are more actve than general practce physcans n seekng nformaton from varous external sources, ncludng drug representatves, n updatng ther preferences about the drug. Fnally, we observe n Table 3 that physcans who practce n hgh-ncome areas have a sgnfcantly hgher degree of carryover coeffcent. [Insert Table 3 here.] 4.1 Clncal Learnng We now turn to the estmates of the clncal learnng parameter K. Recall that ths parameter represents the extent to whch physcan reles on new clncal experence to update the focal drug s preference n perod t (see Equatons 5 7). For each physcan, we calculate the average value of the learnng coeffcent over all the perods (denoted by K). Across our sample of physcans, K ranges from 0.47 to 0.74 wh a mean value of There exsts consderable heterogeney among physcans n the clncal experence-based learnng rates, wh some physcans relyng more on new clncal experence n updatng ther drug effcacy preferences than others. Ths suggests that clncal experence provdes varyng learnng opportunes to 26

28 physcans, possbly because of the dfferences n ther patent profles and the level of access to other nformaton sources. The emprcal results n Table 3 suggest that specalty and local ncome have sgnfcant effects on physcans nformaton gatherng and learnng behavor, whch we analyze next. 4.2 Clncal Learnng wh DSS We now nvestgate the effect of DSS use on the physcan learnng behavor. Recall that the parameters γ and δ are related to the DSS features that support drug selecton and drug admnstraton decsons, respectvely. In Fgure 4a, we fx the value of γ at one and vary δ to llustrate how the average learnng coeffcent (K) vares between specalsts and generalsts and between physcans n hgh- and low-ncome areas. Our clncal learnng model suggests that the role of clncal learnng s lmed when physcans have an establshed preference on the focal drug. Thus, mprovng decson support for the admnstraton of the focal drug (lower δ) strengthens physcan preferences and, n turn, lms the contrbuton of clncal learnng on the prescrpton behavor. As dscussed prevously and as Fgure 4a shows, generalsts and physcans n hgh-ncome areas tend to rely more on ther clncal experence (K values are hgher). In Table 4a, we provde the percent changes on average learnng coeffcents for low and hgh values of δ. The table shows that the negatve effect on clncal learnng behavor s less pronounced for generalsts and physcans n hgh-ncome areas. Thus, ths observaton also mples that generalsts and physcans n hgh-ncome areas may benef more from DSS drug admnstraton features. [Insert Fgure 4 here.] 27

29 [Insert Table 4 here.] Next, n Fgure 4b, we fx the value of δ parameter at one, vary γ, and plot the changes n the average clncal learnng coeffcents for generalsts and physcans n hgh-ncome areas and for specalsts and physcans n low-ncome areas. Table 4b provdes a comparson of the changes n K values. Note that the specalsts and physcans n low-ncome areas are more sensve to changes n both δ and γ. Ths suggests that a DSS wh drug selecton features s lkely to support the learnng of specalsts and physcans n low-ncome areas relatvely more. 5. DISCUSSION AND CONCLUSION Prevous nformaton systems research n health care has examned the busness value of nformaton technology (Devaraj and Kohl 2000) and s adopton whn the sector (Bhattacherjee and Hkmet 2007; Braa et al. 2004, Braa et al. 2007; Hkmet et al. 2008; Hu et al. 1999; Khoumbat et al. 2006; Menachem et al. 2007; Mscone 2007; Reardon and Davdson 2007). We contrbute to ths lerature by nvestgatng when and how DSSs can mprove physcans clncal learnng and thus mprove ther prescrpton decsons n terms of choosng and admnsterng the rght drug. The results have mplcatons on how to ncrease the perceved usefulness of the technology and faclate adopton (Chsmar and Patton 2003; Ong and Wang 2004). Bates et al. (1999) ndcate that physcans tend to be more pragmatc n ther acceptance of the technology. The lerature suggests that physcans value the usefulness of nformaton technologes much more than ther ease of use. Kel et al. (1995, p.89) note that no amount of ease of use wll compensate for low usefulness. Usefulness s typcally operatonalzed as ncreasng physcans productvy, mprovng the qualy of care, and enhancng ther effectveness. Because 28

30 faclatng clncal learnng s perhaps the most crcal benef of DSSs for physcans, we can argue that drug selecton features are of paramount mportance n the adopton of DSSs that are used n the prescrpton process. Usefulness of DSSs s also crcal from an educatonal perspectve. Tech et al. (2000) queston whether DSSs really help the medcal educaton of physcans-n-tranng. They acknowledge that DSSs mprove care n the hospal, but they also suggest that s not known how physcans perform n other settngs whout computerzed decson support after havng been traned wh. One possbly s that physcans learn some facts less well because of ther growng dependence on the computer to supply mportant peces of nformaton. Another possbly s that clncal learnng s enhanced because gudelnes and recommendatons are frequently re-presented and renforced at crucal moments. By focusng on the benefs of DSSs on clncal learnng n ths study, we tend to support the latter argument n that physcans should be able to carry over ther mproved sklls to settngs that lack a DSS. Our learnng model provdes analytcal justfcaton for the exstng emprcal results n the lerature that assocate DSS adopton wh reduced ADE rates. Dfferences n clncal use of DSSs have been documented emprcally. For example, Grant et al. (2006) fnd that prmary care physcans are assocated wh greater use of DSSs. The results of ths study provde mplcatons on whch types of decson support offer more potental for whch categores of physcans and, correspondngly, on whch DSS mplementatons are more lkely to fal. Despe the documented benefs and the mandates, the wdespread clncal acceptance of DSSs has been lackng, and ths has been a concern for researchers and medcal nformatcans (Anderson 1987; Kaplan 2001; Kaushal et al. 2003). A recent study estmates that under current condons, computerzed order entry adopton n urban hospals wll not reach 80 percent penetraton untl 29

31 2029 (Ford et al. 2008). There s a clear need to help faclate the adopton, and our results can help polcy makers desgn better ncentves and mechansms so that they can dentfy and target physcans who stand to benef the most from computerzed decson support. Our emprcal estmatons hghlght the mportance of physcan-level dfferences and salent physcan characterstcs that affect clncal behavor and DSS use. Coscell et al. (2000) argue that clncal DSSs should be vewed as soco-techncal systems n whch an ndvdual physcan s socal background and demographcs also play a role n the success of the adopton of the system (Coscell et al. 2000). In a somewhat related ven, our results show that physcan specalty and locaton have sgnfcant effects on the overall physcan response to nformaton sources and that the specalsts and physcans n low-ncome areas are lkely to benef more from decson support on drug selecton than general practce physcans and physcans located n hgh-ncome areas. Ray et al. (1976) report that more than one-quarter of offce-based Tennessee physcans ms-prescrbed an antbotc (.e., tetracyclne), whch s assocated wh permanent dscoloraton of developng teeth, to young chldren, and Ray et al. (1976) show that rural famly and general practoners faced a hgh rsk of prescrbng these and other agents (e.g., chloramphencol) n a potentally unsafe manner. Such fndngs llustrate that general practoners may have more room for learnng about pharmacology than specalsts, n whch case they may benef more from computerzed decson support over tme, n lne wh our results. However, because DSS use lengthens the duraton of a physcan patent encounter (Sntchenko et al. (2004) has shown that takes 245 and 113 seconds to make a decson wh and whout the DSS, respectvely), such benefs may be dffcult to realze n low-ncome areas that typcally exhb relatvely hgh 30

32 demand for physcan servces. Thus, DSS developers should ncorporate ther products nto physcan workflows well, especally when targetng physcans who work n low-ncome areas. Ths study offers several future research drectons. An mmedate applcaton of the learnng model presented heren s the ndvdual dentfcaton of the physcans who would benef the most from decson support and those who may be dstracted by. Such a targeted approach may faclate the dffuson of DSS adopton and may provde new avenues to overcome the adopton dffcultes (Patterson et al. 2004). Researchers can also apply our learnng model to specfc types of DSSs to understand the role of more detaled aspects of these systems on physcan learnng. For example, researchers can examne, both analytcally and by observng actual physcan behavor, whch types of DSSs (e.g., optmzaton systems, expert systems, data mnng tools) and whch form of recommendaton systems (e.g., those that employ collaboratve-flterng versus content search through machne learnng) are most promsng from a learnng perspectve. Addonally, a smlar methodology can be used to nvestgate the role of DSSs used for physcan tranng. The dynamc nature of our model makes a suable framework for capturng the relatve mportance of external nformaton sources (over tme) n supportng physcans prescrbng decsons. A smlar methodology can also be used to explore the optmal recency, frequency, and amount of tranng needed for each physcan. Fnally, wh suable modfcatons, the methodology developed here can be appled to understand and mprove the professonal learnng of other knowledge workers. 31

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38 Fgure 2. An Illustraton of Prescrpton Behavor Model Fgure 2a: Prescrbng the Focal Drug: No Impact of Clncal Experence Fgure 2b: Prescrbng the Focal Drug: Wh Clncal Learnng 37

39 Fgure 3. Dfference between Preference and Clncal Gudelnes for Dfferent DSS Features 38